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├── FULL_LIST.md
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## Table of Contents

- [Awesome Trustworthy Deep Learning Paper List](#awesome-trustworthy--deep-learning)
  - [Survey](#survey)
  - [Out-of-Distribution Generalization](#out-of-distribution-generalization)
  - [Evasion Attacks and Defenses](#evasion-attacks-and-defenses)
  - [Poisoning Attacks and Defenses](#poisoning-attacks-and-defenses)
  - [Privacy](#privacy)
  - [Fairness](#fairness)
  - [Interpretability](#interpretability)
  - [Alignment](#alignment)
  - [Others](#others)


## Survey

<details>
<summary>Survey: 2024</summary>

- Privacy Computing Meets Metaverse: Necessity, Taxonomy and Challenges. [[paper](https://doi.org/10.1016/j.adhoc.2024.103457)] [[code](https://github.com/6lyc/Awesome-Privacy-Computing-in-Metaverse)]
  - Chuan Chen, Yuecheng Li, Zhenpeng Wu, Chengyuan Mai, Youming Liu, Yanming Hu, Jiawen Kang, Zibin Zheng.  
  - Key Word: Metaverse; Privacy Computing; AI Safety.  
  - <details><summary>Digest</summary> This paper explores the critical role of privacy computing in addressing data security challenges within the metaverse. It categorizes privacy computing technologies into federated learning, differential privacy, homomorphic encryption, and zero-knowledge proofs, analyzing their applications in metaverse scenarios like social interaction, tourism, and virtual economy. The study highlights the necessity of integrating these technologies to mitigate privacy risks and outlines future research directions, including decentralized security mechanisms and the trade-off between privacy protection and user experience.</details>

- NeuroAI for AI Safety. [[paper]](https://arxiv.org/abs/2411.18526)
  - Patrick Mineault, Niccolò Zanichelli, Joanne Zichen Peng, Anton Arkhipov, Eli Bingham, Julian Jara-Ettinger, Emily Mackevicius, Adam Marblestone, Marcelo Mattar, Andrew Payne, Sophia Sanborn, Karen Schroeder, Zenna Tavares, Andreas Tolias.
  - Key Word: AI Safety; Neuroscience; Robustness; Speicificaiton; Assurance.
  - <details><summary>Digest</summary> This paper highlights the potential of neuroscience to inspire advancements in AI safety, emphasizing the brain’s unique mechanisms for robustness, safe exploration, pragmatics, and cooperation. The authors propose leveraging neuroscience for AI safety through brain-inspired representations, architectures, robust sensory-motor systems, fine-tuning with brain data, interpretability methods, and scaling cognitively-inspired designs. Concrete recommendations are provided to integrate neuroscience into AI safety research.

</details>


<details>
<summary>Survey: 2023</summary>

- Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives. [[paper]](https://arxiv.org/abs/2307.16851)
  - Haoyang Liu, Maheep Chaudhary, Haohan Wang.
  - Key Word: Causality; Data-Centric AI.
  - <details><summary>Digest</summary> The paper provides a systematic review of advancements in trustworthy machine learning, covering various areas like robustness, security, interpretability, and fairness, and highlights the shortcomings of traditional empirical risk minimization (ERM) training in handling data challenges. The survey connects these methods using Pearl's hierarchy of causality as a unifying framework, presents a unified language and mathematical vocabulary to link methods across different subfields, explores trustworthiness in large pretrained models, and discusses potential future aspects of the field.

- An Overview of Catastrophic AI Risks. [[paper]](https://arxiv.org/abs/2306.12001)
  - Dan Hendrycks, Mantas Mazeika, Thomas Woodside.
  - Key Word: AI Risks.
  - <details><summary>Digest</summary> The paper provides an overview of catastrophic AI risks, categorizing them into four main groups: malicious use, AI race, organizational risks, and rogue AIs. It discusses specific hazards, presents illustrative stories, envisions ideal scenarios, and proposes practical suggestions for mitigating these risks, aiming to foster a comprehensive understanding and inspire proactive efforts for risk mitigation.

- Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation. [[paper]](https://arxiv.org/abs/2305.02231)
  - Natalia Díaz-Rodríguez, Javier Del Ser, Mark Coeckelbergh, Marcos López de Prado, Enrique Herrera-Viedma, Francisco Herrera.
  - Key Word: AI Ethics; AI Regulation; Responsible AI.
  - <details><summary>Digest</summary> Attaining trustworthy Artificial Intelligence (AI) requires meeting seven technical requirements sustained over three main pillars, including being lawful, ethical, and robust both technically and socially. However, achieving truly trustworthy AI involves a more holistic vision that considers the trustworthiness of all processes and actors involved in the system's life cycle. This multidisciplinary approach contemplates four essential axes, including global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the seven requirements analyzed from a triple perspective. Additionally, a responsible AI system is introduced through a given auditing process, which is subject to the challenges posed by the use of regulatory sandboxes. This work emphasizes the importance of a regulation debate and serves as an entry point to this crucial field for the present and future progress of our society.

- A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy. [[paper]](https://arxiv.org/abs/2302.10637)
  - Yifei Zhang, Dun Zeng, Jinglong Luo, Zenglin Xu, Irwin King.
  - Key Word: Federated Learning; Robustness; Privacy.
  - <details><summary>Digest</summary> We propose a comprehensive roadmap for developing trustworthy FL systems and summarize existing efforts from three key aspects: security, robustness, and privacy. We outline the threats that pose vulnerabilities to trustworthy federated learning across different stages of development, including data processing, model training, and deployment.

- Adversarial Machine Learning: A Systematic Survey of Backdoor Attack, Weight Attack and Adversarial Example. [[paper]](https://arxiv.org/abs/2302.09457)
  - Baoyuan Wu, Li Liu, Zihao Zhu, Qingshan Liu, Zhaofeng He, Siwei Lyu.
  - Key Word: Backdoor Attack; Deployment-Time Adversarial Attack; Inference-Time Adversarial Attack.
  - <details><summary>Digest</summary> In this work, we aim to provide a unified perspective to the AML community to systematically review the overall progress of this field. We firstly provide a general definition about AML, and then propose a unified mathematical framework to covering existing attack paradigms. According to the proposed unified framework, we can not only clearly figure out the connections and differences among these paradigms, but also systematically categorize and review existing works in each paradigm.

</details>

<details>
<summary>Survey: 2022</summary>

- A Comprehensive Survey on Trustworthy Recommender Systems. [[paper]](https://arxiv.org/abs/2209.10117)
  - Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingran Su, Jingtong Gao, Lin Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, Qing Li.
  - Key Word: Recommender Systems; Survey.
  - <details><summary>Digest</summary> We provide a comprehensive overview of Trustworthy Recommender systems (TRec) with a specific focus on six of the most important aspects; namely, Safety & Robustness, Nondiscrimination & Fairness, Explainability, Privacy, Environmental Well-being, and Accountability & Auditability. For each aspect, we summarize the recent related technologies and discuss potential research directions to help achieve trustworthy recommender systems in the future.

- Non-Imaging Medical Data Synthesis for Trustworthy AI: A Comprehensive Survey. [[paper]](https://arxiv.org/abs/2209.09239)
  - Xiaodan Xing, Huanjun Wu, Lichao Wang, Iain Stenson, May Yong, Javier Del Ser, Simon Walsh, Guang Yang.
  - Key Word: Non-Imaging Medical Data Generation; Healthcare; Survey.
  - <details><summary>Digest</summary> State-of-the-art data synthesis algorithms, especially deep learning algorithms, focus more on imaging data while neglecting the synthesis of non-imaging healthcare data, including clinical measurements, medical signals and waveforms, and electronic healthcare records (EHRs). Thus, in this paper, we will review the synthesis algorithms, particularly for non-imaging medical data, with the aim of providing trustworthy AI in this domain. This tutorial-styled review paper will provide comprehensive descriptions of non-imaging medical data synthesis on aspects including algorithms, evaluations, limitations and future research directions.

- Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability. [[paper]](https://arxiv.org/abs/2209.08025)
  - Mengdi Xu, Zuxin Liu, Peide Huang, Wenhao Ding, Zhepeng Cen, Bo Li, Ding Zhao.
  - Key Word: Reinforcement Learning; Distribution Shifts; Robustness; Safety; Survey.
  - <details><summary>Digest</summary> A trustworthy reinforcement learning algorithm should be competent in solving challenging real-world problems, including {robustly} handling uncertainties, satisfying {safety} constraints to avoid catastrophic failures, and {generalizing} to unseen scenarios during deployments. This study aims to overview these main perspectives of trustworthy reinforcement learning considering its intrinsic vulnerabilities on robustness, safety, and generalizability. 

- Trustworthy Recommender Systems. [[paper]](https://arxiv.org/abs/2208.06265)
  - Shoujin Wang, Xiuzhen Zhang, Yan Wang, Huan Liu, Francesco Ricci.
  - Key Word: Survey; Recommender Systems; Trustworthy Recommendation.
  - <details><summary>Digest</summary> Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, system bias. As a result, it has become clear that a strict focus on RS accuracy is limited and the research must consider other important factors, e.g., trustworthiness. For end users, a trustworthy RS (TRS) should not only be accurate, but also transparent, unbiased and fair as well as robust to noise or attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, researchers lack a systematic overview and discussion of the literature in this novel and fast developing field of TRSs. To this end, in this paper, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area.

- Trustworthy Graph Neural Networks: Aspects, Methods and Trends. [[paper]](https://arxiv.org/abs/2205.07424)
  - He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, Jian Pei.
  - Key Word: Survey; Graph Neural Networks.
  - <details><summary>Digest</summary> We propose a comprehensive roadmap to build trustworthy GNNs from the view of the various computing technologies involved. In this survey, we introduce basic concepts and comprehensively summarise existing efforts for trustworthy GNNs from six aspects, including robustness, explainability, privacy, fairness, accountability, and environmental well-being. Additionally, we highlight the intricate cross-aspect relations between the above six aspects of trustworthy GNNs. Finally, we present a thorough overview of trending directions for facilitating the research and industrialisation of trustworthy GNNs.

- A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges. [[paper]](https://arxiv.org/abs/2205.03824)
  - Zhenghua Chen, Min Wu, Alvin Chan, Xiaoli Li, Yew-Soon Ong.
  - Key Word: Survey; Sustainability.
  - <details><summary>Digest</summary> The technical trend in realizing the successes has been towards increasing complex and large size AI models so as to solve more complex problems at superior performance and robustness. This rapid progress, however, has taken place at the expense of substantial environmental costs and resources. Besides, debates on the societal impacts of AI, such as fairness, safety and privacy, have continued to grow in intensity. These issues have presented major concerns pertaining to the sustainable development of AI. In this work, we review major trends in machine learning approaches that can address the sustainability problem of AI.

</details>

<!-- ### Survey: 2021 -->

<details>
<summary>Survey: 2021</summary>

- A Survey on AI Assurance. [[paper]](https://arxiv.org/abs/2111.07505)
  - Feras A. Batarseh, Laura Freeman. *Journal of Big Data*
  - Key Word: Survey; Validation; Verification; Testing; Assurance.
  - <details><summary>Digest</summary> Artificial Intelligence (AI) algorithms are increasingly providing decision making and operational support across multiple domains. AI includes a wide library of algorithms for different problems. One important notion for the adoption of AI algorithms into operational decision process is the concept of assurance. The literature on assurance, unfortunately, conceals its outcomes within a tangled landscape of conflicting approaches, driven by contradicting motivations, assumptions, and intuitions. Accordingly, albeit a rising and novel area, this manuscript provides a systematic review of research works that are relevant to AI assurance, between years 1985 - 2021, and aims to provide a structured alternative to the landscape.

- Trustworthy AI: From Principles to Practices. [[paper]](https://arxiv.org/abs/2110.01167)
  - Bo Li, Peng Qi, Bo Liu, Shuai Di, Jingen Liu, Jiquan Pei, Jinfeng Yi, Bowen Zhou.
  - Key Word: Survey.
  - <details><summary>Digest</summary> In this review, we strive to provide AI practitioners a comprehensive guide towards building trustworthy AI systems. We first introduce the theoretical framework of important aspects of AI trustworthiness, including robustness, generalization, explainability, transparency, reproducibility, fairness, privacy preservation, alignment with human values, and accountability. We then survey leading approaches in these aspects in the industry. To unify the current fragmented approaches towards trustworthy AI, we propose a systematic approach that considers the entire lifecycle of AI systems, ranging from data acquisition to model development, to development and deployment, finally to continuous monitoring and governance.

- Trustworthy AI: A Computational Perspective. [[paper]](https://arxiv.org/abs/2107.06641)
  - Haochen Liu, Yiqi Wang, Wenqi Fan, Xiaorui Liu, Yaxin Li, Shaili Jain, Yunhao Liu, Anil K. Jain, Jiliang Tang.
  - Key Word: Survey.
  - <details><summary>Digest</summary> We present a comprehensive survey of trustworthy AI from a computational perspective, to help readers understand the latest technologies for achieving trustworthy AI. Trustworthy AI is a large and complex area, involving various dimensions. In this work, we focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.

- Causal Learning for Socially Responsible AI. [[paper]](https://arxiv.org/abs/2104.12278)
  - Lu Cheng, Ahmadreza Mosallanezhad, Paras Sheth, Huan Liu. *IJCAI 2021*
  - Key Word: Survey; Bias Mitigation; Transparency.
  - <details><summary>Digest</summary> To make AI address ethical challenges and shun undesirable outcomes, researchers proposed to develop socially responsible AI (SRAI). One of these approaches is causal learning (CL). We survey state-of-the-art methods of CL for SRAI. We begin by examining the seven CL tools to enhance the social responsibility of AI, then review how existing works have succeeded using these tools to tackle issues in developing SRAI such as fairness. The goal of this survey is to bring forefront the potentials and promises of CL for SRAI.

</details>

<!-- ### Survey: 2020 -->

<details>
<summary>Survey: 2020</summary>

- Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context. [[paper]](https://arxiv.org/abs/2007.08911)
  - Ehsan Toreini, Mhairi Aitken, Kovila P. L. Coopamootoo, Karen Elliott, Vladimiro Gonzalez Zelaya, Paolo Missier, Magdalene Ng, Aad van Moorsel.
  - Key Word: Survey.
  - <details><summary>Digest</summary>  In this paper we provide an overview of technologies that support building trustworthy machine learning systems, i.e., systems whose properties justify that people place trust in them. We argue that four categories of system properties are instrumental in achieving the policy objectives, namely fairness, explainability, auditability and safety & security (FEAS). We discuss how these properties need to be considered across all stages of the machine learning life cycle, from data collection through run-time model inference.

- Trust in Data Science: Collaboration, Translation, and Accountability in Corporate Data Science Projects. [[paper]](https://arxiv.org/abs/2002.03389)
  - Samir Passi, Steven J. Jackson.
  - Key Word: Survey; Data Science.
  - <details><summary>Digest</summary> The trustworthiness of data science systems in applied and real-world settings emerges from the resolution of specific tensions through situated, pragmatic, and ongoing forms of work. Drawing on research in CSCW, critical data studies, and history and sociology of science, and six months of immersive ethnographic fieldwork with a corporate data science team, we describe four common tensions in applied data science work: (un)equivocal numbers, (counter)intuitive knowledge, (in)credible data, and (in)scrutable models. We show how organizational actors establish and re-negotiate trust under messy and uncertain analytic conditions through practices of skepticism, assessment, and credibility.

- Artificial Intelligence for Social Good: A Survey. [[paper]](https://arxiv.org/abs/2001.01818)
  - Zheyuan Ryan Shi, Claire Wang, Fei Fang.
  - Key Word: Survey; Social Good.
  - <details><summary>Digest</summary> Artificial intelligence for social good (AI4SG) is a research theme that aims to use and advance artificial intelligence to address societal issues and improve the well-being of the world. AI4SG has received lots of attention from the research community in the past decade with several successful applications.

</details>

<!-- ### Survey: 2019 -->

<details>
<summary>Survey: 2019 and Before</summary>

- The relationship between trust in AI and trustworthy machine learning technologies. [[paper]](https://arxiv.org/abs/1912.00782)
  - Ehsan Toreini, Mhairi Aitken, Kovila Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, Aad van Moorsel. *FAT 2020*
  - Key Word: Survey; Social Science.
  - <details><summary>Digest</summary> To build AI-based systems that users and the public can justifiably trust one needs to understand how machine learning technologies impact trust put in these services. To guide technology developments, this paper provides a systematic approach to relate social science concepts of trust with the technologies used in AI-based services and products. We conceive trust as discussed in the ABI (Ability, Benevolence, Integrity) framework and use a recently proposed mapping of ABI on qualities of technologies. We consider four categories of machine learning technologies, namely these for Fairness, Explainability, Auditability and Safety (FEAS) and discuss if and how these possess the required qualities.

- A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability. [[paper]](https://arxiv.org/abs/1812.08342)
  - Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, Xinping Yi. *Computer Science Review*
  - Key Word: Survey.
  - <details><summary>Digest</summary> This survey paper conducts a review of the current research effort into making DNNs safe and trustworthy, by focusing on four aspects: verification, testing, adversarial attack and defence, and interpretability. In total, we survey 202 papers, most of which were published after 2017.

</details>

## Out-of-Distribution Generalization

<details>
<summary>Out-of-Distribution Generalization: 2024</summary>

- Benign Overfitting in Out-of-Distribution Generalization of Linear Models. [[paper]](https://arxiv.org/abs/2412.14474)
  - Shange Tang, Jiayun Wu, Jianqing Fan, Chi Jin.
  - Key Word: Benign Overfitting; Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> This paper extends the theoretical understanding of benign overfitting—where over-parameterized models fit noisy training data perfectly but still generalize well—to the Out-of-Distribution (OOD) regime, focusing on linear models under covariate shift. The authors provide non-asymptotic guarantees showing that benign overfitting can occur in standard ridge regression when target covariances meet specific structural conditions. They identify key factors influencing OOD generalization and demonstrate that their results recover prior in-distribution and under-parameterized OOD findings. Additionally, they analyze a broader class of target covariances, showing that while ridge regression achieves a slow statistical rate, Principal Component Regression (PCR) achieves a faster rate for excess risk.

- The Pitfalls of Memorization: When Memorization Hurts Generalization. [[paper]](https://arxiv.org/abs/2412.07684)
  - Reza Bayat, Mohammad Pezeshki, Elvis Dohmatob, David Lopez-Paz, Pascal Vincent.
  - Key Word: Memorization; Generalization; Spurious Correlation.
  - <details><summary>Digest</summary> This paper investigates the relationship between memorization and generalization in neural networks, highlighting that reliance on spurious correlations combined with memorization harms generalization. To address this, the authors propose Memorization-Aware Training (MAT), which adjusts model logits based on held-out predictions to discourage memorization and promote learning robust, distribution-invariant patterns, thereby enhancing generalization under distribution shifts.

- Is Large-Scale Pretraining the Secret to Good Domain Generalization? [[paper]](https://arxiv.org/abs/2412.02856)
  - Piotr Teterwak, Kuniaki Saito, Theodoros Tsiligkaridis, Bryan A. Plummer, Kate Saenko.
  - Key Word: Domain Generalization; Pretraining.
  - <details><summary>Digest</summary> This paper examines Multi-Source Domain Generalization (DG), where models are trained on multiple source domains to generalize to unseen target domains. It questions whether recent DG improvements stem from better methods or stronger pretraining and finds that perceptual similarity to pretraining data is insufficient for strong performance. Instead, the authors propose the Alignment Hypothesis, which asserts that DG performance depends on the alignment of image and class label text embeddings. Experiments confirm this hypothesis, revealing that existing methods perform well on data similar to pretraining (IP) but struggle on dissimilar data (OOP). The findings emphasize the need for DG methods capable of generalizing beyond pretraining alignment.

- Transformation-Invariant Learning and Theoretical Guarantees for OOD Generalization. [[paper]](https://arxiv.org/abs/2410.23461)
  - Omar Montasser, Han Shao, Emmanuel Abbe.
  - Key Word: Out-of-Distribution Generalization; Distributionally Robust Optimization.
  - <details><summary>Digest</summary> This paper studies statistical learning under distribution shifts, focusing on scenarios where training and testing distributions are related by data transformation maps. It introduces theoretical learning rules and reductions to Empirical Risk Minimization (ERM), providing sample complexity bounds based on the VC dimension of combined predictors and transformations. The results offer a game-theoretic perspective, where a learner selects predictors to minimize worst-case loss while an adversary selects transformations to maximize it.

- Compositional Risk Minimization. [[paper]](https://arxiv.org/abs/2410.06303)
  - Divyat Mahajan, Mohammad Pezeshki, Ioannis Mitliagkas, Kartik Ahuja, Pascal Vincent.
  - Key Word: Compositional Shifts.
  - <details><summary>Digest</summary> This paper addresses compositional shift, an extreme form of subpopulation shift where certain combinations of attributes are absent in the training data but appear in the test data. The authors propose compositional risk minimization (CRM), a new approach that builds on additive energy distributions to model data attributes. First, they train a classifier to predict attributes and then adjust it to handle compositional shifts. Their theoretical analysis shows that CRM can generalize to unseen attribute combinations, and empirical results demonstrate that CRM improves robustness compared to existing methods for handling subpopulation shifts.

- Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts. [[paper]](https://arxiv.org/abs/2409.13728)
  - Anna Mészáros, Szilvia Ujváry, Wieland Brendel, Patrik Reizinger, Ferenc Huszár.
  - Key Word: Compositional Generalization; Rule Extrapolation; Large Language Model.
  - <details><summary>Digest</summary> This paper explores the out-of-distribution (OOD) behavior of autoregressive large language models (LLMs), focusing on a new concept termed rule extrapolation. Rule extrapolation involves prompts that violate at least one rule in formal languages, which are defined by intersecting rules. The authors evaluate how different architectures, including linear, recurrent, Transformer, and state space models, handle rule extrapolation across various levels of complexity. Additionally, they aim to develop a normative theory of rule extrapolation, drawing inspiration from the Solomonoff prior in algorithmic information theory. The study seeks to deepen understanding of LLMs’ OOD generalization abilities beyond just the Transformer architecture.

- Beyond Discrepancy: A Closer Look at the Theory of Distribution Shift. [[paper]](https://arxiv.org/abs/2405.19156)
  - Robi Bhattacharjee, Nick Rittler, Kamalika Chaudhuri.
  - Key Word: Distribution Shift; Invariant Risk Minimization.
  - <details><summary>Digest</summary> This paper examines the theory of distribution shift for classifiers, proposing an Invariant-Risk-Minimization (IRM)-like assumption to determine when source data alone is sufficient for accurate target classification. It also provides conditions and theoretical guarantees for when unlabeled or labeled target data is necessary, addressing gaps in traditional learning theory.

- Domain Generalisation via Imprecise Learning. [[paper]](https://arxiv.org/abs/2404.04669)
  - Anurag Singh, Siu Lun Chau, Shahine Bouabid, Krikamol Muandet.
  - Key Word: Domain Generalization; Imprecise Learning.
  - <details><summary>Digest</summary> The paper introduces the Imprecise Domain Generalisation framework to address the challenge of out-of-distribution (OOD) generalisation in machine learning. It proposes an imprecise risk optimisation approach that allows learners to optimise against a continuous spectrum of generalisation strategies during training. The framework also allows model operators to specify their generalisation preference at deployment. The work showcases the benefits of integrating imprecision into domain generalisation through theoretical and empirical evidence.

- A Survey on Evaluation of Out-of-Distribution Generalization. [[paper]](https://arxiv.org/abs/2403.01874)
  - Han Yu, Jiashuo Liu, Xingxuan Zhang, Jiayun Wu, Peng Cui.
  - Key Word: Survey; Out-of-Distribution Generalization Evaluation.
  - <details><summary>Digest</summary> OOD generalization involves not only assessing a model's OOD generalization strength but also identifying where it generalizes well or poorly, including the types of distribution shifts it can handle and the safe versus risky input regions. This paper represents the first comprehensive review of OOD evaluation, categorizing existing research into three paradigms based on test data availability and briefly discussing OOD evaluation for pretrained models. It concludes with suggestions for future research directions in OOD evaluation.

</details>


<details>
<summary>Out-of-Distribution Generalization: 2023</summary>

- Unraveling the Key Components of OOD Generalization via Diversification. [[paper]](https://arxiv.org/abs/2312.16313)
  - Harold Benoit, Liangze Jiang, Andrei Atanov, Oğuzhan Fatih Kar, Mattia Rigotti, Amir Zamir.
  - Key Word: Out-of-Distribution Generalization; Spurious Correlation; Diversification.
  - <details><summary>Digest</summary> This paper investigates diversification methods for handling multiple features in real-world datasets. It finds that these methods are sensitive to the distribution of unlabeled data and can underperform when not in an optimal setting. Diversification alone is insufficient for out-of-distribution (OOD) generalization; the choice of learning algorithm, model architecture, and pretraining is crucial. The optimal choice depends on the unlabeled data. Increasing the number of diverse hypotheses doesn't necessarily solve these issues. These findings provide insights into improving OOD generalization with diversification methods and offer guidance to practitioners and researchers in their use and development.

- Causal-structure Driven Augmentations for Text OOD Generalization. [[paper]](https://arxiv.org/abs/2310.12803)
  - Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria, David Blei.
  - Key Word: Out-of-Distribution Generalization; Causality; Data Augmentation.
  - <details><summary>Digest</summary> The paper proposes using counterfactual data augmentation to improve the robustness of text classifiers in situations where they rely on spurious correlations. This approach is particularly relevant in prediction problems where the label is spuriously correlated with an attribute. The authors discuss the advantages of counterfactual data augmentation in terms of sample complexity compared to other methods. They use auxiliary data and a large language model to represent conditional probabilities of text. Experimental results on caregiver-invariant predictors for clinical diagnoses show that this method enhances out-of-distribution accuracy compared to baseline techniques. 

- Does CLIP's Generalization Performance Mainly Stem from High Train-Test Similarity? [[paper]](https://arxiv.org/abs/2310.09562)
  - Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge, Wieland Brendel.
  - Key Word: Measuring Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> The paper discusses CLIP, a foundation model, and its generalization capabilities on out-of-distribution benchmarks. It questions the significance of out-of-distribution generalization in CLIP, suggesting that web-scale datasets may contain samples similar to common benchmarks. The authors retrain CLIP on pruned data that replicates ImageNet's train-test similarity with OOD benchmarks, noting a performance drop on some benchmarks. Surprisingly, CLIP maintains high overall performance, indicating that factors beyond train-test similarity contribute to its generalization. Pruning dissimilar data reveals a smaller dataset on which CLIP can match its original OOD performance. 

- Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness. [[paper]](https://arxiv.org/abs/2310.06161)
  - Bhavya Vasudeva, Kameron Shahabi, Vatsal Sharan.
  - Key Word: Simplicity Bias; Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> The paper discusses the issue of simplicity bias in neural networks, where they tend to favor 'simple' features over more 'complex' but informative ones, leading to biased predictions and poor out-of-distribution (OOD) generalization. To mitigate this bias, the paper introduces a framework that involves training a simple model and then using conditional mutual information regularization to obtain the final model. This approach effectively counters simplicity bias, encourages the use of a wider range of features, improves OOD generalization, and enhances subgroup robustness and fairness in various problem settings and real-world applications. The paper also provides theoretical analysis of the regularization's impact and OOD generalization properties.

- Deep Neural Networks Tend To Extrapolate Predictably. [[paper]](https://arxiv.org/abs/2310.00873)
  - Katie Kang, Amrith Setlur, Claire Tomlin, Sergey Levine.
  - Key Word: Extrapolation; Out-of-Distribution Generalization; Optimal Constant Solution.
  - <details><summary>Digest</summary> This paper challenges the common belief that neural network predictions become unpredictable and overconfident with out-of-distribution (OOD) inputs. Instead, it finds that neural network predictions often stabilize around a constant value as data becomes more OOD, which closely approximates the optimal constant solution (OCS). This phenomenon is demonstrated across various datasets, loss functions, and architectures. The paper offers an explanation for this behavior, validates it empirically, and provides insights for enabling risk-sensitive decision-making in the presence of OOD inputs.

- Spurious Feature Diversification Improves Out-of-distribution Generalization. [[paper]](https://arxiv.org/abs/2309.17230)
  - Yong Lin, Lu Tan, Yifan Hao, Honam Wong, Hanze Dong, Weizhong Zhang, Yujiu Yang, Tong Zhang.
  - Key Word: Spurious Correlation; Out-of-Distribution Generalization; Ensemble-based Models.
  - <details><summary>Digest</summary> This paper addresses the challenge of generalizing to out-of-distribution (OOD) data in machine learning. It focuses on the WiSE-FT ensemble method, which interpolates between pre-trained and fine-tuned models. The study discovers that WiSE-FT corrects cases where individual models make incorrect predictions, improving OOD performance. Theoretical analysis in a multi-class setting with spurious features predicts this phenomenon and suggests that ensemble models benefit from diverse spurious features. Contrary to conventional wisdom, incorporating many diverse spurious features weakens their individual contributions, leading to better OOD performance. Empirical results on MultiColorMNIST dataset support these findings. Additionally, the paper introduces a novel method called BAlaNced averaGing (BANG) to address overconfidence issues in fine-tuned models, further enhancing OOD performance in WiSE-FT.

- Robust Learning with Progressive Data Expansion Against Spurious Correlation. [[paper](https://arxiv.org/abs/2306.04949)]
  - Yihe Deng*, Yu Yang*, Baharan Mirzasoleiman, Quanquan Gu.
  - Key Word: Spurious Correlation; Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> This paper theoretically examine the learning process of a two-layer nonlinear convolutional neural network in the presence of spurious features. Its analysis suggests that imbalanced data groups and easily learnable spurious features can lead to the dominance of spurious features during the early learning process. In light of this, it propose a new training algorithm called PDE that efficiently enhances the model's robustness for a better worst-group performance. PDE begins with a group-balanced subset of training data and progressively expands it to facilitate the learning of the core features. Experiments on synthetic and real-world benchmark datasets confirm the superior performance of our method on models such as ResNets and Transformers.

- Context $\approx$ Environment. [[paper]](https://arxiv.org/abs/2309.09888)
  - Sharut Gupta, Stefanie Jegelka, David Lopez-Paz, Kartik Ahuja.
  - Key Word: In-Context Learning; Invaraint Learning; Domain Generalization.
  - <details><summary>Digest</summary> This paper discusses two key areas in AI research: 1) efforts to build models that generalize better and avoid spurious correlations, and 2) the role of large language models (LLMs) in on-the-fly contextual learning. The paper argues that context is akin to the environment and suggests that in-context learning is essential for improved domain generalization. The proposed In-Context Risk Minimization (ICRM) algorithm, by paying attention to context, enhances out-of-distribution performance. The main takeaways are that domain generalization researchers should consider the environment as context, and LLM researchers should view context as an environment for improved data structuring towards generalization.

- AUC-Oriented Domain Adaptation: From Theory to Algorithm. [[paper]](https://ieeexplore.ieee.org/abstract/document/10214340)
  - Zhiyong Yang, Qianqian Xu, Wenzheng Hou, Shilong Bao, Yuan He, Xiaochun Cao, Qingming Huang. *TPAMI*
  - Key Word: Domain Adaptation; AUC-Oriented Learning.
  - <details><summary>Digest</summary> This paper addresses the use of Area Under the ROC curve (AUC) as a crucial metric in machine learning, particularly for applications with long-tail datasets. It focuses on AUC-oriented Unsupervised Domain Adaptation (UDA) and introduces a new distributional discrepancy for AUC. The paper overcomes the challenge of interdependency in AUC risk and provides a sharper bound with fewer assumptions about the loss function. To make it practical, a pseudo-labeling strategy and an end-to-end training framework are proposed for scenarios where complete annotations on the target domain are unavailable. Empirical studies on five real-world datasets demonstrate the effectiveness of the framework.

- Domain Generalization without Excess Empirical Risk. [[paper]](https://arxiv.org/abs/2308.15856)
  - Ozan Sener, Vladlen Koltun.
  - Key Word: Domain Generalization; Rate-Distortion Theory.
  - <details><summary>Digest</summary> This paper addresses domain generalization with diverse data distributions. It introduces a new approach that separates penalty minimization from empirical risk optimization to prevent excess risk. The method leverages rate-distortion theory for efficient optimization and demonstrates improved performance on existing methods.

- Understanding Hessian Alignment for Domain Generalization. [[paper]](https://arxiv.org/abs/2308.11778)
  - Sobhan Hemati, Guojun Zhang, Amir Estiri, Xi Chen. *ICCV 2023*
  - Key Word: Hessian Alignment; Robust Optimization.
  - <details><summary>Digest</summary> This paper explores out-of-distribution (OOD) generalization in deep learning models, which is crucial in real-world applications like healthcare and autonomous vehicles. It focuses on gradient-based regularizers' effectiveness and analyzes the role of Hessian and gradient alignment in domain generalization. The study establishes that the spectral norm between the classifier's head Hessian matrices across domains is an upper bound of the transfer measure. The success of various regularizers is explained by their alignment of Hessians and gradients. The paper proposes efficient methods for aligning Hessians and gradients, achieving promising OOD generalization results in different scenarios. 

- PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization. [[paper]](https://arxiv.org/abs/2307.15199)
  - Junhyeong Cho, Gilhyun Nam, Sungyeon Kim, Hunmin Yang, Suha Kwak. *ICCV 2023*
  - Key Word: Source-free; Domain Generalization; Prompt Learning; Vision-Language.
  - <details><summary>Digest</summary> In a joint vision-language space, a text feature (e.g., from "a photo of a dog") could effectively represent its relevant image features (e.g., from dog photos). Inspired by this, this paper proposes PromptStyler which simulates various distribution shifts in the joint space by synthesizing diverse styles via prompts without using any images to deal with source-free domain generalization. 

- Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features. [[paper]](https://arxiv.org/abs/2307.09933)
  - Cian Eastwood, Shashank Singh, Andrei Liviu Nicolicioiu, Marin Vlastelica, Julius von Kügelgen, Bernhard Schölkopf.
  - Key Word: Spurious Correlation; Invariant Learning.
  - <details><summary>Digest</summary> The paper introduces Stable Feature Boosting (SFB), an algorithm that leverages both stable and unstable features in the test domain without requiring labels for the test domain. By proving that pseudo-labels based on stable features are sufficient guidance, and given the conditional independence of stable and unstable features with respect to the label, SFB learns an asymptotically-optimal predictor. Empirical results demonstrate the effectiveness of SFB on real and synthetic data for avoiding failures on out-of-distribution data.

- Simple and Fast Group Robustness by Automatic Feature Reweighting. [[paper]](https://arxiv.org/abs/2306.11074)
  - Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson. *ICML 2023*
  - Key Word: Group Robustness; Spurious Correlation; Feature Reweighting.
  - <details><summary>Digest</summary> The paper proposes Automatic Feature Reweighting (AFR), a simple and fast method for reducing the reliance on spurious features in machine learning models. By retraining the last layer of a standard model with a weighted loss that emphasizes poorly predicted examples, AFR automatically upweights the minority group without requiring group labels. Experimental results demonstrate improved performance compared to existing methods on vision and natural language classification benchmarks.

- Optimal Transport Model Distributional Robustness. [[paper]](https://arxiv.org/abs/2306.04178)
  - Van-Anh Nguyen, Trung Le, Anh Tuan Bui, Thanh-Toan Do, Dinh Phung.
  - Key Word: Distributional Robustness; Optimal Transport; Sharpness-Aware Minimization.
  - <details><summary>Digest</summary> This work explores an optimal transport-based distributional robustness framework on model spaces, aiming to enhance the robustness of deep learning models against adversarial examples and data distribution shifts. Previous research has mainly focused on data space, but this study investigates the model distribution within a Wasserstein ball centered around a given model distribution. The authors develop theories to learn the optimal robust center model distribution, allowing for the incorporation of sharpness awareness into various model architectures such as single models, ensembles, and Bayesian Neural Networks. The framework encompasses sharpness-aware minimization (SAM) as a specific case and extends it to a probabilistic setting. Extensive experiments demonstrate significant improvements over baseline methods in different settings.

- Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization. [[paper]](https://arxiv.org/abs/2306.02595)
  - Yimeng Chen, Tianyang Hu, Fengwei Zhou, Zhenguo Li, Zhiming Ma.
  - Key Word: Domain Generalization; Model Zoo; Pre-Trained Models.
  - <details><summary>Digest</summary> This paper addresses the challenge of effectively utilizing a wide range of publicly available pretrained models to enhance out-of-distribution generalization in downstream tasks. While previous research has focused on identifying the most powerful models, this study argues that even weaker models contain valuable knowledge. The authors propose a method that leverages the diversity within the model zoo by analyzing the variations in encoded representations across different domains. By characterizing these variations in terms of diversity shift and correlation shift, they develop an algorithm for integrating diverse pretrained models, not limited to the strongest ones, to improve out-of-distribution generalization. 

- Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models. [[paper]](https://arxiv.org/abs/2305.17076)
  - Waïss Azizian, Franck Iutzeler, Jérôme Malick.
  - Key Word: Wasserstein Distributionally Robust Optimization.
  - <details><summary>Digest</summary> This paper examines the generalization properties of Wasserstein distributionally robust estimators, which are models that optimize prediction and decision-making under uncertainty. The authors show that these estimators have robust objective functions that bound the true risk with high probability, and that these bounds do not depend on the dimensionality of the problem, the class of models, or the distribution shift at testing. They also extend their results to regularized versions of Wasserstein distributionally robust problems.

- Rethinking the Evaluation Protocol of Domain Generalization. [[paper]](https://arxiv.org/abs/2305.15253)
  - Han Yu, Xingxuan Zhang, Renzhe Xu, Jiashuo Liu, Yue He, Peng Cui.
  - Key Word: Domain Generalization; Evaluation Protocol.
  - <details><summary>Digest</summary> Domain generalization aims to solve the challenge of Out-of-Distribution (OOD) generalization. To accurately evaluate the OOD generalization ability, it is necessary to ensure that test data information is unavailable. However, the current domain generalization protocol may still have potential test data information leakage. This paper examines the potential risks of test data information leakage in two aspects of the current protocol. We propose that training from scratch and using multiple test domains would result in a more precise evaluation of OOD generalization ability.

- Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape. [[paper]](https://arxiv.org/abs/2305.11584)
  - Yan Sun, Li Shen, Shixiang Chen, Liang Ding, Dacheng Tao.
  - Key Word: Sharpness Aware Minimization; Federated Learning.
  - <details><summary>Digest</summary> we propose a novel and general algorithm FedSMOO by jointly considering the optimization and generalization targets to efficiently improve the performance in FL. Concretely, FedSMOO adopts a dynamic regularizer to guarantee the local optima towards the global objective, which is meanwhile revised by the global Sharpness Aware Minimization (SAM) optimizer to search for the consistent flat minima. 

- On the nonlinear correlation of ML performance between data subpopulations. [[paper]](https://arxiv.org/abs/2305.02995)
  - Weixin Liang, Yining Mao, Yongchan Kwon, Xinyu Yang, James Zou. *ICML 2023*
  - Key Word: Correlations between ID and OOD Performances; Accuracy-on-the-Line.
  - <details><summary>Digest</summary> This study shows that the correlation between in-distribution (ID) and out-of-distribution (OOD) accuracies is more nuanced under subpopulation shifts than previously thought. The non-trivial nonlinear correlation holds across model architectures, hyperparameters, training durations, and the imbalance between subpopulations. The nonlinearity of the "moon shape" is influenced by spurious correlations in the training data. Understanding the nonlinear effects of model improvement on performance in different subpopulations is important for developing more equitable and responsible machine learning models.

- An Adaptive Algorithm for Learning with Unknown Distribution Drift. [[paper]](https://arxiv.org/abs/2305.02252)
  - Alessio Mazzetto, Eli Upfal.
  - Key Word: Unknown Distribution Drift.
  - <details><summary>Digest</summary> We develop and analyze a general technique for learning with an unknown distribution drift. Given a sequence of independent observations from the last T steps of a drifting distribution, our algorithm agnostically learns a family of functions with respect to the current distribution at time T. Unlike previous work, our technique does not require prior knowledge about the magnitude of the drift. 

- PGrad: Learning Principal Gradients For Domain Generalization. [[paper]](https://arxiv.org/abs/2305.01134) [[code]](https://github.com/QData/PGrad)
  - Zhe Wang, Jake Grigsby, Yanjun Qi. *ICLR 2023*
  - Key Word: Domain Generalization; Gradient Manipulation.
  - <details><summary>Digest</summary> We develop a novel DG training strategy, we call PGrad, to learn a robust gradient direction, improving models' generalization ability on unseen domains. The proposed gradient aggregates the principal directions of a sampled roll-out optimization trajectory that measures the training dynamics across all training domains. PGrad's gradient design forces the DG training to ignore domain-dependent noise signals and updates all training domains with a robust direction covering main components of parameter dynamics. 

- Benchmarking Low-Shot Robustness to Natural Distribution Shifts. [[paper]](https://arxiv.org/abs/2304.11263)
  - Aaditya Singh, Kartik Sarangmath, Prithvijit Chattopadhyay, Judy Hoffman.
  - Key Word: Natural Distribution Shifts; Data-Efficient Fine-Tuning; Benchmarks.
  - <details><summary>Digest</summary> Robustness to natural distribution shifts has seen remarkable progress thanks to recent pre-training strategies combined with better fine-tuning methods. However, such fine-tuning assumes access to large amounts of labelled data, and the extent to which the observations hold when the amount of training data is not as high remains unknown. We address this gap by performing the first in-depth study of robustness to various natural distribution shifts in different low-shot regimes: spanning datasets, architectures, pre-trained initializations, and state-of-the-art robustness interventions.

- Reweighted Mixup for Subpopulation Shift. [[paper]](https://arxiv.org/abs/2304.04148)
  - Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, Qinghua Hu, Bingzhe Wu, Changqing Zhang, Jianhua Yao.
  - Key Word: Mixup; Subpopulation Shift.
  - <details><summary>Digest</summary> We propose a simple yet practical framework, called reweighted mixup (RMIX), to mitigate the overfitting issue in over-parameterized models by conducting importance weighting on the ''mixed'' samples. Benefiting from leveraging reweighting in mixup, RMIX allows the model to explore the vicinal space of minority samples more, thereby obtaining more robust model against subpopulation shift. 

- ERM++: An Improved Baseline for Domain Generalization. [[paper]](https://arxiv.org/abs/2304.01973)
  - Piotr Teterwak, Kuniaki Saito, Theodoros Tsiligkaridis, Kate Saenko, Bryan A. Plummer.
  - Key Word: Domain Generalization; Benchmarking; Weight Averaging; Regularization.
  - <details><summary>Digest</summary> Recent work has shown that a well-tuned Empirical Risk Minimization (ERM) training procedure, that is simply minimizing the empirical risk on the source domains, can outperform most existing DG methods. We identify several key candidate techniques to further improve ERM performance, such as better utilization of training data, model parameter selection, and weight-space regularization. We call the resulting method ERM++, and show it significantly improves the performance of DG on five multi-source datasets by over 5% compared to standard ERM, and beats state-of-the-art despite being less computationally expensive. 

- Domain Generalization via Nuclear Norm Regularization. [[paper]](https://arxiv.org/abs/2303.07527)
  - Zhenmei Shi, Yifei Ming, Ying Fan, Frederic Sala, Yingyu Liang.
  - Key Word: Domain Generalization; Nuclear Norm Regularization; Low-Rank Regularization.
  - <details><summary>Digest</summary> We propose a simple and effective regularization method based on the nuclear norm of the learned features for domain generalization. Intuitively, the proposed regularizer mitigates the impacts of environmental features and encourages learning domain-invariant features. Theoretically, we provide insights into why nuclear norm regularization is more effective compared to ERM and alternative regularization methods. 

- Statistical Learning under Heterogenous Distribution Shift. [[paper]](https://arxiv.org/abs/2302.13934)
  - Max Simchowitz, Anurag Ajay, Pulkit Agrawal, Akshay Krishnamurthy.
  - Key Word: Heterogenous Covariate Shifts; Statistical Learning Theory.
  - <details><summary>Digest</summary> This paper studies the prediction of a target z from a pair of random variables (x,y), where the ground-truth predictor is additive E[z∣x,y]=f⋆(x)+g⋆(y). We study the performance of empirical risk minimization (ERM) over functions f+g, f∈F and g∈G, fit on a given training distribution, but evaluated on a test distribution which exhibits covariate shift. 

- Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights? [[paper]](https://arxiv.org/abs/2302.12480)
  - Ruisi Cai, Zhenyu Zhang, Zhangyang Wang.
  - Key Word: Corruption Robustness; Task Vector.
  - <details><summary>Digest</summary> Our work is dedicated to investigating how natural corruption “robustness” is encoded in weights and how to disentangle/transfer them. We introduce “Robust Weight Signature”(RWS), which nontrivially generalizes the prior wisdom in model weight interpolation and arithmetic, to analyzing standard/robust models, with both methodological innovations and new key findings. RWSs lead to a powerful in-situ model patching framework to easily achieve on-demand robustness towards a wide range of corruptions.

- Change is Hard: A Closer Look at Subpopulation Shift. [[paper]](https://arxiv.org/abs/2302.12254) [[code]](https://github.com/YyzHarry/SubpopBench)
  - Yuzhe Yang, Haoran Zhang, Dina Katabi, Marzyeh Ghassemi.
  - Key Word: Subpopulation Shift; Benchmark.
  - <details><summary>Digest</summary> We provide a fine-grained analysis of subpopulation shift. We first propose a unified framework that dissects and explains common shifts in subgroups. We then establish a comprehensive benchmark of 20 state-of-the-art algorithms evaluated on 12 real-world datasets in vision, language, and healthcare domains. With results obtained from training over 10,000 models, we reveal intriguing observations for future progress in this space. 

- On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective. [[paper]](https://arxiv.org/abs/2302.12095) [[code]](https://github.com/microsoft/robustlearn)
  - Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, Binxin Jiao, Yue Zhang, Xing Xie.
  - Key Word: Adversarial Robustness; Out-of-Distribution Generalization; ChatGPT.
  - <details><summary>Digest</summary> ChatGPT is a recent chatbot service released by OpenAI and is receiving increasing attention over the past few months. While evaluations of various aspects of ChatGPT have been done, its robustness, i.e., the performance when facing unexpected inputs, is still unclear to the public. Robustness is of particular concern in responsible AI, especially for safety-critical applications. In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective. 

- Out-of-Domain Robustness via Targeted Augmentations. [[paper]](https://arxiv.org/abs/2302.11861) [[code]](https://github.com/i-gao/targeted-augs)
  - Irena Gao, Shiori Sagawa, Pang Wei Koh, Tatsunori Hashimoto, Percy Liang.
  - Key Word: Out-of-Distribution Generalization; Data Augmentation.
  - <details><summary>Digest</summary> We study principles for designing data augmentations for out-of-domain (OOD) generalization. In particular, we focus on real-world scenarios in which some domain-dependent features are robust, i.e., some features that vary across domains are predictive OOD. For example, in the wildlife monitoring application above, image backgrounds vary across camera locations but indicate habitat type, which helps predict the species of photographed animals. 

- PerAda: Parameter-Efficient and Generalizable Federated Learning Personalization with Guarantees. [[paper]](https://arxiv.org/abs/2302.06637)
  - Chulin Xie, De-An Huang, Wenda Chu, Daguang Xu, Chaowei Xiao, Bo Li, Anima Anandkumar.
  - Key Word: Personalized Federated Learning; Knowledge Distillation.
  - <details><summary>Digest</summary> We propose PerAda, a parameter-efficient pFL framework that reduces communication and computational costs and exhibits superior generalization performance, especially under test-time distribution shifts. PerAda reduces the costs by leveraging the power of pretrained models and only updates and communicates a small number of additional parameters from adapters. PerAda has good generalization since it regularizes each client's personalized adapter with a global adapter, while the global adapter uses knowledge distillation to aggregate generalized information from all clients. 

- Domain Generalization by Functional Regression. [[paper]](https://arxiv.org/abs/2302.04724)
  - Markus Holzleitner, Sergei V. Pereverzyev, Werner Zellinger.
  - Key Word: Domain Generalization; Function-to-Function Regression.
  - <details><summary>Digest</summary> We study domain generalization as a problem of functional regression. Our concept leads to a new algorithm for learning a linear operator from marginal distributions of inputs to the corresponding conditional distributions of outputs given inputs. 

- Federated Minimax Optimization with Client Heterogeneity. [[paper]](https://arxiv.org/abs/2302.04249)
  - Pranay Sharma, Rohan Panda, Gauri Joshi.
  - Key Word: Heterogeneous Federated Minimax Optimization.
  - <details><summary>Digest</summary> We propose a general federated minimax optimization framework that subsumes such settings and several existing methods like Local SGDA. We show that naive aggregation of heterogeneous local progress results in optimizing a mismatched objective function -- a phenomenon previously observed in standard federated minimization. To fix this problem, we propose normalizing the client updates by the number of local steps undertaken between successive communication rounds. 

- Leveraging Domain Relations for Domain Generalization. [[paper]](https://arxiv.org/abs/2302.02609)
  - Huaxiu Yao, Xinyu Yang, Xinyi Pan, Shengchao Liu, Pang Wei Koh, Chelsea Finn.
  - Key Word: Domain Generalization; Ensemble Learning.
  - <details><summary>Digest</summary> We focus on domain shifts, which occur when the model is applied to new domains that are different from the ones it was trained on, and propose a new approach called D^3G. Unlike previous approaches that aim to learn a single model that is domain invariant, D^3G learns domain-specific models by leveraging the relations among different domains. 

- Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. [[paper]](https://arxiv.org/abs/2302.01381)
  - Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin.
  - Key Word: Effective Robustness; Natural Distribution Shifts.
  - <details><summary>Digest</summary> We propose a new effective robustness evaluation metric to compare the effective robustness of models trained on different data distributions. To do this we control for the accuracy on multiple ID test sets that cover the training distributions for all the evaluated models. Our new evaluation metric provides a better estimate of the effectiveness robustness and explains the surprising effective robustness gains of zero-shot CLIP-like models exhibited when considering only one ID dataset, while the gains diminish under our evaluation.

- Free Lunch for Domain Adversarial Training: Environment Label Smoothing. [[paper]](https://arxiv.org/abs/2302.00194)
  - YiFan Zhang, Xue Wang, Jian Liang, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan.
  - Key Word: Domain Adversarial Training; Label Smoothing.
  - <details><summary>Digest</summary> We proposed Environment Label Smoothing (ELS), which encourages the discriminator to output soft probability, which thus reduces the confidence of the discriminator and alleviates the impact of noisy environment labels. We demonstrate, both experimentally and theoretically, that ELS can improve training stability, local convergence, and robustness to noisy environment labels.

- FedFA: Federated Feature Augmentation. [[paper]](https://arxiv.org/abs/2301.12995) [[code]](https://github.com/tfzhou/FedFA)
  - Tianfei Zhou, Ender Konukoglu. *ICLR 2023*
  - Key Word: Federated Learning; Feature Augmentation; Feature Shifts.
  - <details><summary>Digest</summary> The primary goal of this paper is to develop a robust federated learning algorithm to address feature shift in clients' samples, which can be caused by various factors, e.g., acquisition differences in medical imaging. To reach this goal, we propose FedFA to tackle federated learning from a distinct perspective of federated feature augmentation. FedFA is based on a major insight that each client's data distribution can be characterized by statistics (i.e., mean and standard deviation) of latent features; and it is likely to manipulate these local statistics globally, i.e., based on information in the entire federation, to let clients have a better sense of the underlying distribution and therefore alleviate local data bias.

- Alignment with human representations supports robust few-shot learning. [[paper]](https://arxiv.org/abs/2301.11990)
  - Ilia Sucholutsky, Thomas L. Griffiths. 
  - Key Word: Representational Alignment; Few-Shot Learning; Domain Shifts; Adversarial Robustness.
  - <details><summary>Digest</summary> Should we care whether AI systems have representations of the world that are similar to those of humans? We provide an information-theoretic analysis that suggests that there should be a U-shaped relationship between the degree of representational alignment with humans and performance on few-shot learning tasks. We confirm this prediction empirically, finding such a relationship in an analysis of the performance of 491 computer vision models.

- DEJA VU: Continual Model Generalization For Unseen Domains. [[paper]](https://arxiv.org/abs/2301.10418) [[code]](https://github.com/dawnliu35/dejavu-ratp)
  - Chenxi Liu, Lixu Wang, Lingjuan Lyu, Chen Sun, Xiao Wang, Qi Zhu.
  - Key Word: Domain Generalization; Domain Adaptation.
  - <details><summary>Digest</summary> Existing DG works are ineffective for continually changing domains due to severe catastrophic forgetting of learned knowledge. To overcome these limitations of DA and DG in handling the Unfamiliar Period during continual domain shift, we propose RaTP, a framework that focuses on improving models' target domain generalization (TDG) capability, while also achieving effective target domain adaptation (TDA) capability right after training on certain domains and forgetting alleviation (FA) capability on past domains. 

- ManyDG: Many-domain Generalization for Healthcare Applications. [[paper]](https://arxiv.org/abs/2301.08834) [[code]](https://github.com/ycq091044/ManyDG)
  - Chaoqi Yang, M Brandon Westover, Jimeng Sun. *ICLR 2023*
  - Key Word: Domain Generalization; Healthcare.
  - <details><summary>Digest</summary> In healthcare applications, most existing domain generalization methods assume a small number of domains. In this paper, considering the diversity of patient covariates, we propose a new setting by treating each patient as a separate domain (leading to many domains). We develop a new domain generalization method ManyDG, that can scale to such many-domain problems. Our method identifies the patient domain covariates by mutual reconstruction and removes them via an orthogonal projection step.

</details>

<!-- ### Out-of-Distribution Generalization: 2022 -->
<details>
<summary>Out-of-Distribution Generalization: 2022</summary>

- Generalizing to unseen domains: a survey on domain generalization. [[paper]](https://jd92.wang/assets/files/DG_survey_TKDE22.pdf)
  - Jindong Wang , Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin, Wang Lu, Yiqiang Chen, Wenjun Zeng, and Philip S. Yu. *IEEE TKDE 2022*
  - Key Word: Out-of-distribution generalization; Domain generalization
  - <details><summary>Digest</summary> This paper presents the first review of recent advances in this area. First, we provide a formal definition of domain generalization and discuss several related fields. We then thoroughly review the theories related to domain generalization and carefully analyze the theory behind generalization.

- Stable learning establishes some common ground between causal inference and machine learning. [[paper]](https://www.nature.com/articles/s42256-022-00445-z)
  - Peng Cui, Susan Athey. *Nature Machine Intelligence*
  - Key Word: Stable Learning; Causal Inference.
  - <details><summary>Digest</summary> With the aim of bridging the gap between the tradition of precise modelling in causal inference and black-box approaches from machine learning, stable learning is proposed and developed as a source of common ground. This Perspective clarifies a source of risk for machine learning models and discusses the benefits of bringing causality into learning.

- CrossMatch: Cross-Classifier Consistency Regularization for Open-Set Single Domain Generalization. [[paper]](https://openreview.net/forum?id=48RBsJwGkJf)
  - Ronghang Zhu, Sheng Li. *ICLR 2022*
  - Key Word: Single Domain Generalization, Open-Set Recognition.
  - <details><summary>Digest</summary> We propose a challenging and untouched problem: Open-Set Single Domain Generalization (OS-SDG), where target domains include unseen categories out of source label space. The goal of OS-SDG is to learn a model, with only one source domain, to classify a target sample with correct class if it belongs to source label space, or assign it to unknown classes. We design a CrossMatch approach to improve the performance of SDG methods on identifying unknown classes by leveraging a multi-binary classifier.

- Invariant Causal Representation Learning for Out-of-Distribution Generalization. [[paper]](https://openreview.net/forum?id=-e4EXDWXnSn)
  - Chaochao Lu, Yuhuai Wu, José Miguel Hernández-Lobato, Bernhard Schölkopf. *ICLR 2022*
  - Key Word: Out-of-Distribution Generalization; Invariant Causal Prediction; Causal Representation Learning.
  - <details><summary>Digest</summary> We propose invariant Causal Representation Learning (iCaRL), an approach that enables out-of-distribution (OOD) generalization in the nonlinear setting (i.e., nonlinear representations and nonlinear classifiers). It builds upon a practical and general assumption: the prior over the data representation (i.e., a set of latent variables encoding the data) given the target and the environment belongs to general exponential family distributions, i.e., a more flexible conditionally non-factorized prior that can actually capture complicated dependences between the latent variables.

- Target Conditioned Representation Independence (TCRI); From Domain-Invariant to Domain-General Representations. [[paper]](https://arxiv.org/abs/2212.11342)
  - Olawale Salaudeen, Oluwasanmi Koyejo.
  - Key Word: Domain Generalization; Invaraint Leanring.
  - <details><summary>Digest</summary> We propose a Target Conditioned Representation Independence (TCRI) objective for domain generalization. TCRI addresses the limitations of existing domain generalization methods due to incomplete constraints. Specifically, TCRI implements regularizers motivated by conditional independence constraints that are sufficient to strictly learn complete sets of invariant mechanisms, which we show are necessary and sufficient for domain generalization.

- Recycling diverse models for out-of-distribution generalization. [[paper]](https://arxiv.org/abs/2212.10445)
  - Alexandre Ramé, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Léon Bottou, David Lopez-Paz.
  - Key Word: Out-of-Distribution Generalization; Fine-Tuning; Weight Averaging.
  - <details><summary>Digest</summary> We propose model recycling, a simple strategy that leverages multiple fine-tunings of the same foundation model on diverse auxiliary tasks, and repurposes them as rich and diverse initializations for the target task. Specifically, model recycling fine-tunes in parallel each specialized model on the target task, and then averages the weights of all target fine-tunings into a final model. 

- Domain Generalization by Learning and Removing Domain-specific Features. [[paper]](https://arxiv.org/abs/2212.07101) [[code]](https://github.com/yulearningg/LRDG)
  - Yu Ding, Lei Wang, Bin Liang, Shuming Liang, Yang Wang, Fang Chen.
  - Key Word: Domain Generalization; Domain-Specific Features.
  - <details><summary>Digest</summary> We propose a novel framework called Learning and Removing Domain-specific features for Generalization (LRDG) that learns a domain-invariant model by tactically removing domain-specific features from the input images. Specifically, we design a classifier to effectively learn the domain-specific features for each source domain, respectively. We then develop an encoder-decoder network to map each input image into a new image space where the learned domain-specific features are removed. 

- Minimax Optimal Estimation of Stability Under Distribution Shift. [[paper]](https://arxiv.org/abs/2212.06338)
  - Hongseok Namkoong, Yuanzhe Ma, Peter W. Glynn.
  - Key Word: Stability; Robustness; Distributional Uncertainty; Model Misspecification.
  - <details><summary>Digest</summary> The performance of decision policies and prediction models often deteriorates when applied to environments different from the ones seen during training. To ensure reliable operation, we propose and analyze the stability of a system under distribution shift, which is defined as the smallest change in the underlying environment that causes the system's performance to deteriorate beyond a permissible threshold. In contrast to standard tail risk measures and distributionally robust losses that require the specification of a plausible magnitude of distribution shift, the stability measure is defined in terms of a more intuitive quantity: the level of acceptable performance degradation.

- Spuriosity Rankings: Sorting Data for Spurious Correlation Robustness. [[paper]](https://arxiv.org/abs/2212.02648)
  - Mazda Moayeri, Wenxiao Wang, Sahil Singla, Soheil Feizi.
  - Key Word: Mitigating Spurious Correlations; Spurious Correlation Benchmarks; Natural Image-based Interpretability.
  - <details><summary>Digest</summary> We present a framework for ranking images within their class based on the strength of spurious cues present. By measuring the gap in accuracy on the highest and lowest ranked images (we call this spurious gap), we assess spurious feature reliance for 89 diverse ImageNet models, finding that even the best models underperform in images with weak spurious presence. 

- Avoiding spurious correlations via logit correction. [[paper]](https://arxiv.org/abs/2212.01433)
  - Sheng Liu, Xu Zhang, Nitesh Sekhar, Yue Wu, Prateek Singhal, Carlos Fernandez-Granda.
  - Key Word: Mitigating Spurious Correlations; Logit Correction.
  - <details><summary>Digest</summary> We explicitly consider a situation where potential spurious correlations are present in the majority of training data. In contrast with existing approaches, which use the ERM model outputs to detect the samples without spurious correlations, and either heuristically upweighting or upsampling those samples; we propose the logit correction (LC) loss, a simple yet effective improvement on the softmax cross-entropy loss, to correct the sample logit. 

- Malign Overfitting: Interpolation Can Provably Preclude Invariance. [[paper]](https://arxiv.org/abs/2211.15724)
  - Yoav Wald, Gal Yona, Uri Shalit, Yair Carmon.
  - Key Word: Invariant Learning; Overfitting; Interpolation.
  - <details><summary>Digest</summary> In this work we provide a theoretical justification for these observations. We prove that -- even in the simplest of settings -- any interpolating learning rule (with arbitrarily small margin) will not satisfy these invariance properties. We then propose and analyze an algorithm that -- in the same setting -- successfully learns a non-interpolating classifier that is provably invariant. We validate our theoretical observations on simulated data and the Waterbirds dataset.

- Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers. [[paper]](https://arxiv.org/abs/2211.15231)
  - Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson. *NeurIPS 2022*
  - Key Word: Shortcut Learning; Generative Classifiers; Group Robustness.
  - <details><summary>Digest</summary> We show that generative models alone are not sufficient to prevent shortcut learning, despite an incentive to recover a more comprehensive representation of the data than discriminative approaches. However, we observe that shortcuts are preferentially encoded with minimal information, a fact that generative models can exploit to mitigate shortcut learning. In particular, we propose Chroma-VAE, a two-pronged approach where a VAE classifier is initially trained to isolate the shortcut in a small latent subspace, allowing a secondary classifier to be trained on the complementary, shortcut-free latent subspace. 

- Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time. [[paper]](https://arxiv.org/abs/2211.14238) [[code]](https://github.com/huaxiuyao/wild-time)
  - Huaxiu Yao, Caroline Choi, Bochuan Cao, Yoonho Lee, Pang Wei Koh, Chelsea Finn. *NeurIPS 2022*
  - Key Word: Temporal Distribution Shift.
  - <details><summary>Digest</summary> By leveraging timestamp metadata, models can potentially learn from trends in past distribution shifts and extrapolate into the future. While recent works have studied distribution shifts, temporal shifts remain underexplored. To address this gap, we curate Wild-Time, a benchmark of 5 datasets that reflect temporal distribution shifts arising in a variety of real-world applications, including patient prognosis and news classification. On these datasets, we systematically benchmark 13 prior approaches, including methods in domain generalization, continual learning, self-supervised learning, and ensemble learning. 

- First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains. [[paper]](https://arxiv.org/abs/2211.11719)
  - Kefan Dong, Tengyu Ma.
  - Key Word: Domain Generalization; Domain Generalization; Extrapolation.
  - <details><summary>Digest</summary> This paper makes some initial steps towards analyzing the extrapolation of nonlinear models for structured domain shift. We primarily consider settings where the marginal distribution of each coordinate of the data (or subset of coordinates) do not shift significantly across the training and test distributions, but the joint distribution may have a much bigger shift. 

- Invariant Learning via Diffusion Dreamed Distribution Shifts. [[paper]](https://arxiv.org/abs/2211.10370)
  - Priyatham Kattakinda, Alexander Levine, Soheil Feizi.
  - Key Word: Foreground and Background; Distribution Shifts; Text-to-Image Diffusion Models.
  - <details><summary>Digest</summary> We propose such a dataset called Diffusion Dreamed Distribution Shifts (D3S). D3S consists of synthetic images generated through StableDiffusion using text prompts and image guides obtained by pasting a sample foreground image onto a background template image. Using this scalable approach we generate 120K images of objects from all 1000 ImageNet classes in 10 diverse backgrounds.

- ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations. [[paper]](https://arxiv.org/abs/2211.01866)
  - Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov, Caner Hazirbas, Nicolas Ballas, Pascal Vincent, Michal Drozdzal, David Lopez-Paz, Mark Ibrahim.
  - Key Word: Out-of-Distribution Generalization; Dataset.
  - <details><summary>Digest</summary> Deep learning vision systems are widely deployed across applications where reliability is critical. However, even today's best models can fail to recognize an object when its pose, lighting, or background varies. While existing benchmarks surface examples challenging for models, they do not explain why such mistakes arise. To address this need, we introduce ImageNet-X, a set of sixteen human annotations of factors such as pose, background, or lighting the entire ImageNet-1k validation set as well as a random subset of 12k training images. 

- Functional Indirection Neural Estimator for Better Out-of-distribution Generalization. [[paper]](https://arxiv.org/abs/2210.12739)
  - Kha Pham, Hung Le, Man Ngo, Truyen Tran. *NeurIPS 2022*
  - Key Word: Out-of-Distribution Generalization; Functional Indirection Neural Estimator.
  - <details><summary>Digest</summary> We hypothesize that OOD generalization may be achieved by performing analogy-making and indirection in the functional space instead of the data space as in current methods. To realize this, we design FINE (Functional Indirection Neural Estimator), a neural framework that learns to compose functions that map data input to output on-the-fly. FINE consists of a backbone network and a trainable semantic memory of basis weight matrices. 

- Just Mix Once: Worst-group Generalization by Group Interpolation. [[paper]](https://arxiv.org/abs/2210.12195)
  - Giorgio Giannone, Serhii Havrylov, Jordan Massiah, Emine Yilmaz, Yunlong Jiao. 
  - Key Word: Out-of-Distribution Generalization; Data Augmentation; Mixup; Group Robustness.
  - <details><summary>Digest</summary> A recent line of work leverages self-supervision and oversampling to improve generalization on minority groups without group annotation. We propose to unify and generalize these approaches using a class-conditional variant of mixup tailored for worst-group generalization. Our approach, Just Mix Once (JM1), interpolates samples during learning, augmenting the training distribution with a continuous mixture of groups. JM1 is domain agnostic and computationally efficient, can be used with any level of group annotation, and performs on par or better than the state-of-the-art on worst-group generalization. 

- On Feature Learning in the Presence of Spurious Correlations. [[paper]](https://arxiv.org/abs/2210.11369) [[code]](https://github.com/izmailovpavel/spurious_feature_learning)
  - Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson. *NeurIPS 2022*
  - Key Word: Spurious Correlations; Feature Learning.
  - <details><summary>Digest</summary> We evaluate the amount of information about the core (non-spurious) features that can be decoded from the representations learned by standard empirical risk minimization (ERM) and specialized group robustness training. Following recent work on Deep Feature Reweighting (DFR), we evaluate the feature representations by re-training the last layer of the model on a held-out set where the spurious correlation is broken. 

- Freeze then Train: Towards Provable Representation Learning under Spurious Correlations and Feature Noise. [[paper]](https://arxiv.org/abs/2210.11075)
  - Haotian Ye, James Zou, Linjun Zhang.
  - Key Word: Feature Learning; Spurious Correlations.
  - <details><summary>Digest</summary> We find that core features are only learned well when they are less noisy than spurious features, which is not necessarily true in practice. We provide both theories and experiments to support this finding and to illustrate the importance of feature noise. Moreover, we propose an algorithm called Freeze then Train (FTT), that first freezes certain salient features and then trains the rest of the features using ERM.

- ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization. [[paper]](https://arxiv.org/abs/2210.09236)
  - Qishi Dong, Awais Muhammad, Fengwei Zhou, Chuanlong Xie, Tianyang Hu, Yongxin Yang, Sung-Ho Bae, Zhenguo Li. *NeurIPS 2022*
  - Key Word: Out-of-Distribution Generalization; Pre-Training.
  - <details><summary>Digest</summary> We propose ZooD, a paradigm for PTMs ranking and ensemble with feature selection. Our proposed metric ranks PTMs by quantifying inter-class discriminability and inter-domain stability of the features extracted by the PTMs in a leave-one-domain-out cross-validation manner. The top-K ranked models are then aggregated for the target OoD task. To avoid accumulating noise induced by model ensemble, we propose an efficient variational EM algorithm to select informative features. We evaluate our paradigm on a diverse model zoo consisting of 35 models for various OoD tasks.

- Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors. [[paper]](https://arxiv.org/abs/2210.06807) [[code]](https://github.com/NOVAglow646/NIPS22-MAT-and-LDAT-for-OOD)
  - Qixun Wang, Yifei Wang, Hong Zhu, Yisen Wang. *NeurIPS 2022*
  - Key Word: Out-of-Distribution Generalization; Adversarial Training.
  - <details><summary>Digest</summary> We empirically show that sample-wise AT has limited improvement on OOD performance. Specifically, we find that AT can only maintain performance at smaller scales of perturbation while Universal AT (UAT) is more robust to larger-scale perturbations. This provides us with clues that adversarial perturbations with universal (low dimensional) structures can enhance the robustness against large data distribution shifts that are common in OOD scenarios. Inspired by this, we propose two AT variants with low-rank structures to train OOD-robust models.

- Revisiting adapters with adversarial training. [[paper]](https://arxiv.org/abs/2210.04886)
  - Sylvestre-Alvise Rebuffi, Francesco Croce, Sven Gowal. 
  - Key Word: Adversarial Training; Adapters.
  - <details><summary>Digest</summary> We improve upon the top-1 accuracy of a non-adversarially trained ViT-B16 model by +1.12% on ImageNet (reaching 83.76% top-1 accuracy). Second, and more importantly, we show that training with adapters enables model soups through linear combinations of the clean and adversarial tokens. These model soups, which we call adversarial model soups, allow us to trade-off between clean and robust accuracy without sacrificing efficiency. Finally, we show that we can easily adapt the resulting models in the face of distribution shifts. 

- FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. [[paper]](https://arxiv.org/abs/2210.04620)
  - Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Teleńczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux. *NeurIPS 2022*
  - Key Word: Federated Learning; Healthcare; Benchmarks.
  - <details><summary>Digest</summary> We propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL. FLamby encompasses 7 healthcare datasets with natural splits, covering multiple tasks, modalities, and data volumes, each accompanied with baseline training code. 

- Coresets for Wasserstein Distributionally Robust Optimization Problems. [[paper]](https://arxiv.org/abs/2210.04260)
  - Ruomin Huang, Jiawei Huang, Wenjie Liu, Hu Ding. *NeurIPS 2022*
  - Key Word: Coresets; Distributionally Robust Optimization.
  - <details><summary>Digest</summary> We introduce a unified framework to construct the ϵ-coreset for the general WDRO problems. Though it is challenging to obtain a conventional coreset for WDRO due to the uncertainty issue of ambiguous data, we show that we can compute a ''dual coreset'' by using the strong duality property of WDRO.

- Attention Diversification for Domain Generalization. [[paper]](https://arxiv.org/abs/2210.04206) [[code]](https://github.com/hikvision-research/DomainGeneralization)
  - Rang Meng, Xianfeng Li, Weijie Chen, Shicai Yang, Jie Song, Xinchao Wang, Lei Zhang, Mingli Song, Di Xie, Shiliang Pu. *ECCV 2022*
  - Key Word: Domain Generalization; Attention Diversification.
  - <details><summary>Digest</summary> We find the devils lie in the fact that models trained on different domains merely bias to different domain-specific features yet overlook diverse task-related features. Under this guidance, a novel Attention Diversification framework is proposed, in which Intra-Model and Inter-Model Attention Diversification Regularization are collaborated to reassign appropriate attention to diverse task-related features. 

- Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints. [[paper]](https://arxiv.org/abs/2210.01413)
  - Jiajin Li, Sirui Lin, Jose Blanchet, Viet Anh Nguyen. *NeurIPS 2022*
  - Key Word: Optimal Transport; Distributionally Robust Optimization; Tikhonov Regularization.
  - <details><summary>Digest</summary> Distributionally robust optimization has been shown to offer a principled way to regularize learning models. In this paper, we find that Tikhonov regularization is distributionally robust in an optimal transport sense (i.e., if an adversary chooses distributions in a suitable optimal transport neighborhood of the empirical measure), provided that suitable martingale constraints are also imposed. Further, we introduce a relaxation of the martingale constraints which not only provides a unified viewpoint to a class of existing robust methods but also leads to new regularization tools. 

- Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. [[paper]](https://arxiv.org/abs/2210.01302)
  - Aahlad Puli, Nitish Joshi, He He, Rajesh Ranganath. 
  - Key Word: Data Augmentation; Robustness to Spurious Correlations.
  - <details><summary>Digest</summary> We develop an alternative way to produce robust models by data augmentation. These data augmentations corrupt semantic information to produce models that identify and adjust for where nuisances drive predictions. We study semantic corruptions in powering different robust-modeling methods for multiple out-of distribution (OOD) tasks like classifying waterbirds, natural language inference, and detecting Cardiomegaly in chest X-rays.

- Federated Representation Learning via Maximal Coding Rate Reduction. [[paper]](https://arxiv.org/abs/2210.00299)
  - Juan Cervino, Navid NaderiAlizadeh, Alejandro Ribeiro.
  - Key Word: Personalized Federated Learning; Maximal Coding Rate Reduction.
  - <details><summary>Digest</summary> We propose a federated methodology to learn low-dimensional representations from a dataset that is distributed among several clients. In particular, we move away from the commonly-used cross-entropy loss in federated learning, and seek to learn shared low-dimensional representations of the data in a decentralized manner via the principle of maximal coding rate reduction (MCR2). Our proposed method, which we refer to as FLOW, utilizes MCR2 as the objective of choice, hence resulting in representations that are both between-class discriminative and within-class compressible. 

- MaskTune: Mitigating Spurious Correlations by Forcing to Explore. [[paper]](https://arxiv.org/abs/2210.00055) [[code]](https://github.com/aliasgharkhani/masktune)
  - Saeid Asgari Taghanaki, Aliasghar Khani, Fereshte Khani, Ali Gholami, Linh Tran, Ali Mahdavi-Amiri, Ghassan Hamarneh. *NeurIPS 2022*
  - Key Word: Input Masking; Robustness to Spurious Correlations.
  - <details><summary>Digest</summary> A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting spurious input features. This work proposes MaskTune, a masking strategy that prevents over-reliance on spurious (or a limited number of) features. MaskTune forces the trained model to explore new features during a single epoch finetuning by masking previously discovered features. MaskTune, unlike earlier approaches for mitigating shortcut learning, does not require any supervision, such as annotating spurious features or labels for subgroup samples in a dataset. 

- Domain Generalization -- A Causal Perspective. [[paper]](https://arxiv.org/abs/2209.15177)
  - Paras Sheth, Raha Moraffah, K. Selçuk Candan, Adrienne Raglin, Huan Liu.
  - Key Word: Domain Generalization; Causality; Survey.
  - <details><summary>Digest</summary> We present a comprehensive survey on causal domain generalization models from the aspects of the problem and causal theories. Furthermore, this survey includes in-depth insights into publicly accessible datasets and benchmarks for domain generalization in various domains. Finally, we conclude the survey with insights and discussions on future research directions. Finally, we conclude the survey with insights and discussions on future research directions.

- A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases. [[paper]](https://arxiv.org/abs/2209.11208)
  - James Harrison, Luke Metz, Jascha Sohl-Dickstein. *NeurIPS 2022*
  - Key Word: Optimizer; Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> We use tools from dynamical systems to investigate the inductive biases and stability properties of optimization algorithms, and apply the resulting insights to designing inductive biases for blackbox optimizers. Our investigation begins with a noisy quadratic model, where we characterize conditions in which optimization is stable, in terms of eigenvalues of the training dynamics. We then introduce simple modifications to a learned optimizer's architecture and meta-training procedure which lead to improved stability, and improve the optimizer's inductive bias.

- UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup. [[paper]](https://arxiv.org/abs/2209.08928)
  - Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu, Lanqing Li, Yatao Bian, Peilin Zhao, Bingzhe Wu, Changqing Zhang, Jianhua Yao. *NeurIPS 2022*
  - Key Word: Importance Weighting; Subpopulation Shift; Mixup.
  - <details><summary>Digest</summary> We propose a simple yet practical framework, called uncertainty-aware mixup (Umix), to mitigate the overfitting issue in over-parameterized models by reweighting the "mixed" samples according to the sample uncertainty. The training-trajectories-based uncertainty estimation is equipped in the proposed Umix for each sample to flexibly characterize the subpopulation distribution. We also provide insightful theoretical analysis to verify that Umix achieves better generalization bounds over prior works.

- Importance Tempering: Group Robustness for Overparameterized Models. [[paper]](https://arxiv.org/abs/2209.08745)
  - Yiping Lu, Wenlong Ji, Zachary Izzo, Lexing Ying.
  - Ke Word: Importance Tempering; Label Shift; Neural Collapse; Spurious Correlations.
  - <details><summary>Digest</summary> We propose importance tempering to improve the decision boundary and achieve consistently better results for overparameterized models. Theoretically, we justify that the selection of group temperature can be different under label shift and spurious correlation setting. At the same time, we also prove that properly selected temperatures can extricate the minority collapse for imbalanced classification. 

- On-Device Domain Generalization. [[paper]](https://arxiv.org/abs/2209.07521) [[code]](https://github.com/KaiyangZhou/on-device-dg)
  - Kaiyang Zhou, Yuanhan Zhang, Yuhang Zang, Jingkang Yang, Chen Change Loy, Ziwei Liu.
  - Key Word: Domain Generalization; Knowledge Distillation.
  - <details><summary>Digest</summary> We find that knowledge distillation is a strong candidate for solving the problem: it outperforms state-of-the-art DG methods that were developed using large models with a large margin. Moreover, we observe that the teacher-student performance gap on test data with domain shift is bigger than that on in-distribution data. To improve DG for tiny neural networks without increasing the deployment cost, we propose a simple idea called out-of-distribution knowledge distillation (OKD), which aims to teach the student how the teacher handles (synthetic) out-of-distribution data and is proved to be a promising framework for solving the problem. 

- ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets. [[paper]](https://arxiv.org/abs/2209.00613)
  - Damien Teney, Seong Joon Oh, Ehsan Abbasnejad.
  - Key Word: Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> This short paper shows that inverse correlations between ID and OOD performance do happen in real-world benchmarks. They may have been missed in past studies because of a biased selection of models. We show an example of the pattern on the WILDS-Camelyon17 dataset, using models from multiple training epochs and random seeds. Our observations are particularly striking on models trained with a regularizer that diversifies the solutions to the ERM objective.

- DR-DSGD: A Distributionally Robust Decentralized Learning Algorithm over Graphs. [[paper]](https://arxiv.org/abs/2208.13810)
  - Chaouki Ben Issaid, Anis Elgabli, Mehdi Bennis. *TMLR*
  - Key Word: Robust Federated Learning; Fairness in Federated Learning; Decentralized Learning.
  - <details><summary>Digest</summary> We propose to solve a regularized distributionally robust learning problem in the decentralized setting, taking into account the data distribution shift. By adding a Kullback-Liebler regularization function to the robust min-max optimization problem, the learning problem can be reduced to a modified robust minimization problem and solved efficiently. Leveraging the newly formulated optimization problem, we propose a robust version of Decentralized Stochastic Gradient Descent (DSGD), coined Distributionally Robust Decentralized Stochastic Gradient Descent (DR-DSGD). 

- Shortcut Learning of Large Language Models in Natural Language Understanding: A Survey. [[paper]](https://arxiv.org/abs/2208.11857)
  - Mengnan Du, Fengxiang He, Na Zou, Dacheng Tao, Xia Hu.
  - Key Word: Survey; Shortcut Learning; Out-of-Distribution Generalization; Large Language Models.
  - <details><summary>Digest</summary> Large language models (LLMs) have achieved state-of-the-art performance on a series of natural language understanding tasks. However, these LLMs might rely on dataset bias and artifacts as shortcuts for prediction. This has significantly hurt their Out-of-Distribution (OOD) generalization and adversarial robustness. In this paper, we provide a review of recent developments that address the robustness challenge of LLMs. We first introduce the concepts and robustness challenge of LLMs. We then introduce methods to identify shortcut learning behavior in LLMs, characterize the reasons for shortcut learning, as well as introduce mitigation solutions. Finally, we identify key challenges and introduce the connections of this line of research to other directions.

- A Unified Causal View of Domain Invariant Representation Learning. [[paper]](https://arxiv.org/abs/2208.06987) [[code]](https://github.com/zihao12/causal_da_code)
  - Zihao Wang, Victor Veitch.
  - Key Word: Causality; Data Augmentation; Invariant Learning.
  - <details><summary>Digest</summary> Machine learning methods can be unreliable when deployed in domains that differ from the domains on which they were trained. To address this, we may wish to learn representations of data that are domain-invariant in the sense that we preserve data structure that is stable across domains, but throw out spuriously-varying parts. There are many representation-learning approaches of this type, including methods based on data augmentation, distributional invariances, and risk invariance. Unfortunately, when faced with any particular real-world domain shift, it is unclear which, if any, of these methods might be expected to work. The purpose of this paper is to show how the different methods relate to each other, and clarify the real-world circumstances under which each is expected to succeed. The key tool is a new notion of domain shift relying on the idea that causal relationships are invariant, but non-causal relationships (e.g., due to confounding) may vary.

- Class Is Invariant to Context and Vice Versa: On Learning Invariance for Out-Of-Distribution Generalization. [[paper]](https://arxiv.org/abs/2208.03462) [[code]](https://github.com/simpleshinobu/irmcon)
  - Jiaxin Qi, Kaihua Tang, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang. *ECCV 2022*
  - Key Word: Invarinat Learning; Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> We argue that the widely adopted assumption in prior work, the context bias can be directly annotated or estimated from biased class prediction, renders the context incomplete or even incorrect. In contrast, we point out the everoverlooked other side of the above principle: context is also invariant to class, which motivates us to consider the classes (which are already labeled) as the varying environments to resolve context bias (without context labels). We implement this idea by minimizing the contrastive loss of intra-class sample similarity while assuring this similarity to be invariant across all classes.

- Self-Distilled Vision Transformer for Domain Generalization. [[paper]](https://arxiv.org/abs/2207.12392) [[code]](https://github.com/maryam089/SDViT)
  - Maryam Sultana, Muzammal Naseer, Muhammad Haris Khan, Salman Khan, Fahad Shahbaz Khan. *ECCV 2022*
  - Key Word: Domain Generalization; Vision Transformers; Self Distillation.
  - <details><summary>Digest</summary> We attempt to explore ViTs towards addressing the DG problem. Similar to CNNs, ViTs also struggle in out-of-distribution scenarios and the main culprit is overfitting to source domains. Inspired by the modular architecture of ViTs, we propose a simple DG approach for ViTs, coined as self-distillation for ViTs. It reduces the overfitting to source domains by easing the learning of input-output mapping problem through curating non-zero entropy supervisory signals for intermediate transformer blocks.

- Equivariance and Invariance Inductive Bias for Learning from Insufficient Data. [[paper]](https://arxiv.org/abs/2207.12258) [[code]](https://github.com/Wangt-CN/EqInv)
  - Tan Wang, Qianru Sun, Sugiri Pranata, Karlekar Jayashree, Hanwang Zhang. *ECCV 2022*
  - Key Word: Visual Inductive Bias; Data-Efficient Learning; Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> First, compared to sufficient data, we show why insufficient data renders the model more easily biased to the limited training environments that are usually different from testing. For example, if all the training swan samples are "white", the model may wrongly use the "white" environment to represent the intrinsic class swan. Then, we justify that equivariance inductive bias can retain the class feature while invariance inductive bias can remove the environmental feature, leaving the class feature that generalizes to any environmental changes in testing. To impose them on learning, for equivariance, we demonstrate that any off-the-shelf contrastive-based self-supervised feature learning method can be deployed; for invariance, we propose a class-wise invariant risk minimization (IRM) that efficiently tackles the challenge of missing environmental annotation in conventional IRM.

- Domain-invariant Feature Exploration for Domain Generalization. [[paper]](https://arxiv.org/abs/2207.12020) [[code]](https://github.com/jindongwang/transferlearning/tree/master/code/DeepDG)
  - Wang Lu, Jindong Wang, Haoliang Li, Yiqiang Chen, Xing Xie.
  - Key Word: Domain Generalization; Fourier Features.
  - <details><summary>Digest</summary> We argue that domain-invariant features should be originating from both internal and mutual sides. Internal invariance means that the features can be learned with a single domain and the features capture intrinsic semantics of data, i.e., the property within a domain, which is agnostic to other domains. Mutual invariance means that the features can be learned with multiple domains (cross-domain) and the features contain common information, i.e., the transferable features w.r.t. other domains.

- Discrete Key-Value Bottleneck. [[paper]](https://arxiv.org/abs/2207.11240)
  - Frederik Träuble, Anirudh Goyal, Nasim Rahaman, Michael Mozer, Kenji Kawaguchi, Yoshua Bengio, Bernhard Schölkopf.
  - Key Word: Distribution Shifts; Catastrophic Forgetting; Memory Augmented Models.
  - <details><summary>Digest</summary> In the present work, we propose a model architecture to address this issue, building upon a discrete bottleneck containing pairs of separate and learnable (key, value) codes. In this setup, we follow the encode; process the representation via a discrete bottleneck; and decode paradigm, where the input is fed to the pretrained encoder, the output of the encoder is used to select the nearest keys, and the corresponding values are fed to the decoder to solve the current task. The model can only fetch and re-use a limited number of these (key, value) pairs during inference, enabling localized and context-dependent model updates.

- Domain generalization for activity recognition via adaptive feature fusion. [[paper]](https://arxiv.org/abs/2207.11221)
  - Xin Qin, Jindong Wang, Yiqiang Chen, Wang Lu, and Xinlong Jiang. *ACM TIST 2022*
  - Key Word: Domain generalization; Activity recognition.
  - <details><summary>Digest</summary> We propose Adaptive Feature Fusion for Activity Recognition (AFFAR), a domain generalization approach that learns to fuse the domain-invariant and domain-specific representations to improve the model’s generalization performance. AFFAR takes the best of both worlds where domain-invariant representations enhance the transferability across domains and domain-specific representations leverage the model discrimination power from each domain. 

- UniFed: A Benchmark for Federated Learning Frameworks. [[paper]](https://arxiv.org/abs/2207.10308) [[code]](https://github.com/ai-secure/flbenchmark-toolkit)
  - Xiaoyuan Liu, Tianneng Shi, Chulin Xie, Qinbin Li, Kangping Hu, Haoyu Kim, Xiaojun Xu, Bo Li, Dawn Song.
  - Key Word: Federated Learning; Benchmark; Privacy.
  - <details><summary>Digest</summary> Federated Learning (FL) has become a practical and popular paradigm in machine learning. However, currently, there is no systematic solution that covers diverse use cases. Practitioners often face the challenge of how to select a matching FL framework for their use case. In this work, we present UniFed, the first unified benchmark for standardized evaluation of the existing open-source FL frameworks. With 15 evaluation scenarios, we present both qualitative and quantitative evaluation results of nine existing popular open-sourced FL frameworks, from the perspectives of functionality, usability, and system performance. We also provide suggestions on framework selection based on the benchmark conclusions and point out future improvement directions.

- Grounding Visual Representations with Texts for Domain Generalization. [[paper]](https://arxiv.org/abs/2207.10285) [[code]](https://github.com/mswzeus/gvrt)
  - Seonwoo Min, Nokyung Park, Siwon Kim, Seunghyun Park, Jinkyu Kim. *ECCV 2022*
  - Key Word: Domain Generalization; Visual and Textual Explanations.
  - <details><summary>Digest</summary> We introduce two modules to ground visual representations with texts containing typical reasoning of humans: (1) Visual and Textual Joint Embedder and (2) Textual Explanation Generator. The former learns the image-text joint embedding space where we can ground high-level class-discriminative information into the model. The latter leverages an explainable model and generates explanations justifying the rationale behind its decision. To the best of our knowledge, this is the first work to leverage the vision-and-language cross-modality approach for the domain generalization task.

- Tackling Long-Tailed Category Distribution Under Domain Shifts. [[paper]](https://arxiv.org/abs/2207.10150) [[code]](https://github.com/guxiao0822/lt-ds)
  - Xiao Gu, Yao Guo, Zeju Li, Jianing Qiu, Qi Dou, Yuxuan Liu, Benny Lo, Guang-Zhong Yang. *ECCV 2022*
  - Key Word: Long-Tailed Category Distribution; Domain Generalization; Cross-Modal Learning.
  - <details><summary>Digest</summary> We took a step forward and looked into the problem of long-tailed classification under domain shifts. We designed three novel core functional blocks including Distribution Calibrated Classification Loss, Visual-Semantic Mapping and Semantic-Similarity Guided Augmentation. Furthermore, we adopted a meta-learning framework which integrates these three blocks to improve domain generalization on unseen target domains.

- Overcoming Shortcut Learning in a Target Domain by Generalizing Basic Visual Factors from a Source Domain. [[paper]](https://arxiv.org/abs/2207.10002) [[code]](https://github.com/boschresearch/sourcegen)
  - Piyapat Saranrittichai, Chaithanya Kumar Mummadi, Claudia Blaiotta, Mauricio Munoz, Volker Fischer. *ECCV 2022*
  - Key Word: Compositional Generalization; Domain Generalization; Learning Independent Representations.
  - <details><summary>Digest</summary> Shortcut learning occurs when a deep neural network overly relies on spurious correlations in the training dataset in order to solve downstream tasks. Prior works have shown how this impairs the compositional generalization capability of deep learning models. To address this problem, we propose a novel approach to mitigate shortcut learning in uncontrolled target domains. Our approach extends the training set with an additional dataset (the source domain), which is specifically designed to facilitate learning independent representations of basic visual factors. We benchmark our idea on synthetic target domains where we explicitly control shortcut opportunities as well as real-world target domains.

- Probable Domain Generalization via Quantile Risk Minimization. [[paper]](https://arxiv.org/abs/2207.09944) [[code]](https://github.com/cianeastwood/qrm)
  - Cian Eastwood, Alexander Robey, Shashank Singh, Julius von Kügelgen, Hamed Hassani, George J. Pappas, Bernhard Schölkopf.
  - Key Word: Domain Generalization; Causality; Invariant Learning.
  - <details><summary>Digest</summary> A recent study found that no DG algorithm outperformed empirical risk minimization in terms of average performance. In this work, we argue that DG is neither a worst-case problem nor an average-case problem, but rather a probabilistic one. To this end, we propose a probabilistic framework for DG, which we call Probable Domain Generalization, wherein our key idea is that distribution shifts seen during training should inform us of probable shifts at test time. To realize this, we explicitly relate training and test domains as draws from the same underlying meta-distribution, and propose a new optimization problem -- Quantile Risk Minimization (QRM) -- which requires that predictors generalize with high probability.

- Assaying Out-Of-Distribution Generalization in Transfer Learning. [[paper]](https://arxiv.org/abs/2207.09239)
  - Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello.
  - Key Word: Out-of-Distribution Generalization; Transfer Learning; Calibration; Adversarial Robustness; Corruption Robustness; Invariant Learning.
  - <details><summary>Digest</summary> Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations. While sharing the same aspirational goal, these approaches have never been tested under the same experimental conditions on real data. In this paper, we take a unified view of previous work, highlighting message discrepancies that we address empirically, and providing recommendations on how to measure the robustness of a model and how to improve it. To this end, we collect 172 publicly available dataset pairs for training and out-of-distribution evaluation of accuracy, calibration error, adversarial attacks, environment invariance, and synthetic corruptions. We fine-tune over 31k networks, from nine different architectures in the many- and few-shot setting.

- On the Strong Correlation Between Model Invariance and Generalization. [[paper]](https://arxiv.org/abs/2207.07065)
  - Weijian Deng, Stephen Gould, Liang Zheng.
  - Key Word: Predicting Generalization Gap; Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> First, we introduce effective invariance (EI), a simple and reasonable measure of model invariance which does not rely on image labels. Given predictions on a test image and its transformed version, EI measures how well the predictions agree and with what level of confidence. Second, using invariance scores computed by EI, we perform large-scale quantitative correlation studies between generalization and invariance, focusing on rotation and grayscale transformations. From a model-centric view, we observe generalization and invariance of different models exhibit a strong linear relationship, on both in-distribution and out-of-distribution datasets. From a dataset-centric view, we find a certain model's accuracy and invariance linearly correlated on different test sets.

- Improved OOD Generalization via Conditional Invariant Regularizer. [[paper]](https://arxiv.org/abs/2207.06687)
  - Mingyang Yi, Ruoyu Wang, Jiachen Sun, Zhenguo Li, Zhi-Ming Ma.
  - Key Word: Out-of-Distribution Generalization; Conditional Spurious Variation.
  - <details><summary>Digest</summary> Recently, generalization on out-of-distribution (OOD) data with correlation shift has attracted great attention. The correlation shift is caused by the spurious attributes that correlate to the class label, as the correlation between them may vary in training and test data. For such a problem, we show that given the class label, the conditionally independent models of spurious attributes are OOD generalizable. Based on this, a metric Conditional Spurious Variation (CSV) which controls OOD generalization error, is proposed to measure such conditional independence. To improve the OOD generalization, we regularize the training process with the proposed CSV.

- Models Out of Line: A Fourier Lens on Distribution Shift Robustness. [[paper]](https://arxiv.org/abs/2207.04075)
  - Sara Fridovich-Keil, Brian R. Bartoldson, James Diffenderfer, Bhavya Kailkhura, Peer-Timo Bremer.
  - Key Word: Predicting Out-of-Distribution Generalization; Frequency Analysis.
  - <details><summary>Digest</summary> There still is no clear understanding of the conditions on OOD data and model properties that are required to observe effective robustness. We approach this issue by conducting a comprehensive empirical study of diverse approaches that are known to impact OOD robustness on a broad range of natural and synthetic distribution shifts of CIFAR-10 and ImageNet. In particular, we view the "effective robustness puzzle" through a Fourier lens and ask how spectral properties of both models and OOD data influence the corresponding effective robustness.

- Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning. [[paper]](https://arxiv.org/abs/2207.02598)
  - Damien Teney, Maxime Peyrard, Ehsan Abbasnejad. *ECCV 2022*
  - Key Word: Out-of-Distribution Generalization; Underspecification; Ensembles; Feature Diversity.
  - <details><summary>Digest</summary> We formalize the concept of underspecification and propose a method to identify and partially address it. We train multiple models with an independence constraint that forces them to implement different functions. They discover predictive features that are otherwise ignored by standard empirical risk minimization (ERM), which we then distill into a global model with superior OOD performance. Importantly, we constrain the models to align with the data manifold to ensure that they discover meaningful features. We demonstrate the method on multiple datasets in computer vision (collages, WILDS-Camelyon17, GQA) and discuss general implications of underspecification. Most notably, in-domain performance cannot serve for OOD model selection without additional assumptions.

- Neural Networks and the Chomsky Hierarchy. [[paper]](https://arxiv.org/abs/2207.02098) [[code]](https://github.com/deepmind/neural_networks_chomsky_hierarchy)
  - Grégoire Delétang, Anian Ruoss, Jordi Grau-Moya, Tim Genewein, Li Kevin Wenliang, Elliot Catt, Marcus Hutter, Shane Legg, Pedro A. Ortega.
  - Key Word: Chomsky Hierarchy; Out-of-Distribution Generalization;
  - <details><summary>Digest</summary> Reliable generalization lies at the heart of safe ML and AI. However, understanding when and how neural networks generalize remains one of the most important unsolved problems in the field. In this work, we conduct an extensive empirical study (2200 models, 16 tasks) to investigate whether insights from the theory of computation can predict the limits of neural network generalization in practice. We demonstrate that grouping tasks according to the Chomsky hierarchy allows us to forecast whether certain architectures will be able to generalize to out-of-distribution inputs.

- Multi-modal Robustness Analysis Against Language and Visual Perturbations. [[paper]](https://arxiv.org/abs/2207.02159) [[code]](https://github.com/Maddy12/MultiModalVideoRobustness/tree/master/code)  
  - Madeline C. Schiappa, Yogesh S. Rawat, Shruti Vyas, Vibhav Vineet, Hamid Palangi.
  - Key Word: Corruption Robustness; Multi-modal Robustness; Text-to-Video Retrieval.
  - <details><summary>Digest</summary> Joint visual and language modeling on large-scale datasets has recently shown a good progress in multi-modal tasks when compared to single modal learning. However, robustness of these approaches against real-world perturbations has not been studied. In this work, we perform the first extensive robustness study of such models against various real-world perturbations focusing on video and language. We focus on text-to-video retrieval and propose two large-scale benchmark datasets, MSRVTT-P and YouCook2-P, which utilize 90 different visual and 35 different textual perturbations.

- Predicting Out-of-Domain Generalization with Local Manifold Smoothness. [[paper]](https://arxiv.org/abs/2207.02093)
  - Nathan Ng, Kyunghyun Cho, Neha Hulkund, Marzyeh Ghassemi.
  - Key Word: Measures of Complexity; Predicting Out-of-Distribution Generalization; Measuring Function Smoothness.
  - <details><summary>Digest</summary> Recent work has proposed a variety of complexity measures that directly predict or theoretically bound the generalization capacity of a model. However, these methods rely on a strong set of assumptions that in practice are not always satisfied. Motivated by the limited settings in which existing measures can be applied, we propose a novel complexity measure based on the local manifold smoothness of a classifier. We define local manifold smoothness as a classifier's output sensitivity to perturbations in the manifold neighborhood around a given test point. Intuitively, a classifier that is less sensitive to these perturbations should generalize better.

- Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology. [[paper]](https://arxiv.org/abs/2206.14973) [[code]](https://github.com/superjamessyx/robustness_benchmark)
  - Yunlong Zhang, Yuxuan Sun, Honglin Li, Sunyi Zheng, Chenglu Zhu, Lin Yang. *MICCAI 2022*
  - Key Word: Corruption Robustness; Digital Pathology.
  - <details><summary>Digest</summary> When designing a diagnostic model for a clinical application, it is crucial to guarantee the robustness of the model with respect to a wide range of image corruptions. Herein, an easy-to-use benchmark is established to evaluate how deep neural networks perform on corrupted pathology images. Specifically, corrupted images are generated by injecting nine types of common corruptions into validation images. Besides, two classification and one ranking metrics are designed to evaluate the prediction and confidence performance under corruption.

- Towards out of distribution generalization for problems in mechanics. [[paper]](https://arxiv.org/abs/2206.14917)
  - Lingxiao Yuan, Harold S. Park, Emma Lejeune.
  - Key Word: Out-of-Distribution Generalization; Invariant Learning.
  - <details><summary>Digest</summary> Out-of-distribution (OOD) generalization assumes that the test data may shift (i.e., violate the i.i.d. assumption). To date, multiple methods have been proposed to improve the OOD generalization of ML methods. However, because of the lack of benchmark datasets for OOD regression problems, the efficiency of these OOD methods on regression problems, which dominate the mechanics field, remains unknown. To address this, we investigate the performance of OOD generalization methods for regression problems in mechanics. Specifically, we identify three OOD problems: covariate shift, mechanism shift, and sampling bias. For each problem, we create two benchmark examples that extend the Mechanical MNIST dataset collection, and we investigate the performance of popular OOD generalization methods on these mechanics-specific regression problems.

- Guillotine Regularization: Improving Deep Networks Generalization by Removing their Head. [[paper]](https://arxiv.org/abs/2206.13378)
  - Florian Bordes, Randall Balestriero, Quentin Garrido, Adrien Bardes, Pascal Vincent.
  - Key Word: Pre-training; Self-Supervion; Fine-tuning; Regularization; Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> One unexpected technique that emerged in recent years consists in training a Deep Network (DN) with a Self-Supervised Learning (SSL) method, and using this network on downstream tasks but with its last few layers entirely removed. This usually skimmed-over trick is actually critical for SSL methods to display competitive performances. For example, on ImageNet classification, more than 30 points of percentage can be gained that way. This is a little vexing, as one would hope that the network layer at which invariance is explicitly enforced by the SSL criterion during training (the last layer) should be the one to use for best generalization performance downstream. But it seems not to be, and this study sheds some light on why. This trick, which we name Guillotine Regularization (GR), is in fact a generically applicable form of regularization that has also been used to improve generalization performance in transfer learning scenarios. In this work, through theory and experiments, we formalize GR and identify the underlying reasons behind its success in SSL methods.

- Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift. [[paper]](https://arxiv.org/abs/2206.13089)
  - Christina Baek, Yiding Jiang, Aditi Raghunathan, Zico Kolter.
  - Key Word: estimating Generalization Error; Distribution Shift.
  - <details><summary>Digest</summary> Recently, Miller et al. showed that a model's in-distribution (ID) accuracy has a strong linear correlation with its out-of-distribution (OOD) accuracy on several OOD benchmarks -- a phenomenon they dubbed ''accuracy-on-the-line''. While a useful tool for model selection (i.e., the model most likely to perform the best OOD is the one with highest ID accuracy), this fact does not help estimate the actual OOD performance of models without access to a labeled OOD validation set. In this paper, we show a similar but surprising phenomenon also holds for the agreement between pairs of neural network classifiers: whenever accuracy-on-the-line holds, we observe that the OOD agreement between the predictions of any two pairs of neural networks (with potentially different architectures) also observes a strong linear correlation with their ID agreement.

- Memory-Guided Multi-View Multi-Domain Fake News Detection. [[paper]](https://arxiv.org/abs/2206.12808)
  - Yongchun Zhu, Qiang Sheng, Juan Cao, Qiong Nan, Kai Shu, Minghui Wu, Jindong Wang, and Fuzhen Zhuang. *IEEE TKDE 2022*
  - Key Word: Multi-domain learning; out-of-distribution generalization.
  - <details><summary>Digest</summary> We propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M3FEND) to address these two challenges. We model news pieces from a multi-view perspective, including semantics, emotion, and style. Specifically, we propose a Domain Memory Bank to enrich domain information which could discover potential domain labels based on seen news pieces and model domain characteristics. Then, with enriched domain information as input, a Domain Adapter could adaptively aggregate discriminative information from multiple views for news in various domains.

- Gated Domain Units for Multi-source Domain Generalization. [[paper]](https://arxiv.org/abs/2206.12444)
  - Simon Föll, Alina Dubatovka, Eugen Ernst, Martin Maritsch, Patrik Okanovic, Gudrun Thäter, Joachim M. Buhmann, Felix Wortmann, Krikamol Muandet.
  - Key Word: Multi-Source Domain Generalization; Invariant Elementary Distributions.
  - <details><summary>Digest</summary> Distribution shift (DS) is a common problem that deteriorates the performance of learning machines. To overcome this problem, we postulate that real-world distributions are composed of elementary distributions that remain invariant across different domains. We call this an invariant elementary distribution (I.E.D.) assumption. This invariance thus enables knowledge transfer to unseen domains. To exploit this assumption in domain generalization (DG), we developed a modular neural network layer that consists of Gated Domain Units (GDUs). Each GDU learns an embedding of an individual elementary domain that allows us to encode the domain similarities during the training. During inference, the GDUs compute similarities between an observation and each of the corresponding elementary distributions which are then used to form a weighted ensemble of learning machines.

- On Certifying and Improving Generalization to Unseen Domains. [[paper]](https://arxiv.org/abs/2206.12364) [[code]](https://github.com/akshaymehra24/CertifiableDG)
  - Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm.
  - Key Word: Certified Domain Generalization; Distributionally Robust Optimization.
  - <details><summary>Digest</summary> We demonstrate that the accuracy of the models trained with DG methods varies significantly across unseen domains, generated from popular benchmark datasets. This highlights that the performance of DG methods on a few benchmark datasets may not be representative of their performance on unseen domains in the wild. To overcome this roadblock, we propose a universal certification framework based on distributionally robust optimization (DRO) that can efficiently certify the worst-case performance of any DG method. This enables a data-independent evaluation of a DG method complementary to the empirical evaluations on benchmark datasets.

- Out of distribution robustness with pre-trained Bayesian neural networks. [[paper]](https://arxiv.org/abs/2206.12361)
  - Xi Wang, Laurence Aitchison.
  - Key Word: Corruption Robustness; Pre-training; Bayesian Neural Networks.
  - <details><summary>Digest</summary> We develop ShiftMatch, a new training-data-dependent likelihood for out of distribution (OOD) robustness in Bayesian neural networks (BNNs). ShiftMatch is inspired by the training-data-dependent "EmpCov" priors from Izmailov et al. (2021a) and efficiently matches test-time spatial correlations to those at training time. Critically, ShiftMatch is designed to leave neural network training unchanged, allowing it to use publically available samples from pretrained BNNs. Using pre-trained HMC samples, ShiftMatch gives strong performance improvements on CIFAR-10-C, outperforms EmpCov priors, and is perhaps the first Bayesian method capable of convincingly outperforming plain deep ensembles.

- Invariant Causal Mechanisms through Distribution Matching. [[paper]](https://arxiv.org/abs/2206.11646)
  - Mathieu Chevalley, Charlotte Bunne, Andreas Krause, Stefan Bauer.
  - Key Word: Domain Generalization; Causal Inference.
  - <details><summary>Digest</summary> Learning representations that capture the underlying data generating process is a key problem for data efficient and robust use of neural networks. One key property for robustness which the learned representation should capture and which recently received a lot of attention is described by the notion of invariance. In this work we provide a causal perspective and new algorithm for learning invariant representations. Empirically we show that this algorithm works well on a diverse set of tasks and in particular we observe state-of-the-art performance on domain generalization, where we are able to significantly boost the score of existing models.

- On Pre-Training for Federated Learning. [[paper]](https://arxiv.org/abs/2206.11488)
  - Hong-You Chen, Cheng-Hao Tu, Ziwei Li, Han-Wei Shen, Wei-Lun Chao.
  - Key Word: Pre-training; Federated Learning; Training with Sythetic Data.
  - <details><summary>Digest</summary> In most of the literature on federated learning (FL), neural networks are initialized with random weights. In this paper, we present an empirical study on the effect of pre-training on FL. Specifically, we aim to investigate if pre-training can alleviate the drastic accuracy drop when clients' decentralized data are non-IID. We focus on FedAvg, the fundamental and most widely used FL algorithm. We found that pre-training does largely close the gap between FedAvg and centralized learning under non-IID data, but this does not come from alleviating the well-known model drifting problem in FedAvg's local training. Instead, how pre-training helps FedAvg is by making FedAvg's global aggregation more stable. When pre-training using real data is not feasible for FL, we propose a novel approach to pre-train with synthetic data.

- Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming. [[paper]](https://arxiv.org/abs/2206.10816) [[code]](https://github.com/AlvinWen428/fighting-fire-with-fire)
  - Chuan Wen, Jianing Qian, Jierui Lin, Jiaye Teng, Dinesh Jayaraman, Yang Gao. *ICML 2022*
  - Key Word: Shortcut Removal; Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> We show empirically that DNNs can be coaxed to avoid poor shortcuts by providing an additional "priming" feature computed from key input features, usually a coarse output estimate. Priming relies on approximate domain knowledge of these task-relevant key input features, which is often easy to obtain in practical settings. For example, one might prioritize recent frames over past frames in a video input for visual imitation learning, or salient foreground over background pixels for image classification.

- Mitigating Data Heterogeneity in Federated Learning with Data Augmentation. [[paper]](https://arxiv.org/abs/2206.09979)
  - Artur Back de Luca, Guojun Zhang, Xi Chen, Yaoliang Yu.
  - Key Word: Federated Learning; Domain Generalization; Data Augmentation.
  - <details><summary>Digest</summary> While many approaches in DG tackle data heterogeneity from the algorithmic perspective, recent evidence suggests that data augmentation can induce equal or greater performance. Motivated by this connection, we present federated versions of popular DG algorithms, and show that by applying appropriate data augmentation, we can mitigate data heterogeneity in the federated setting, and obtain higher accuracy on unseen clients. Equipped with data augmentation, we can achieve state-of-the-art performance using even the most basic Federated Averaging algorithm, with much sparser communication.

- Decoupled Federated Learning for ASR with Non-IID Data. [[paper]](https://arxiv.org/abs/2206.09102)
  - Han Zhu, Jindong Wang , Gaofeng Cheng, Pengyuan Zhang, and Yonghong Yan. *Interspeech 2022*
  - Key Word: Non-IID; Federated learning; Speech recognition.
  - <details><summary>Digest</summary> We tackle the non-IID issue in FL-based ASR with personalized FL, which learns personalized models for each client. Concretely, we propose two types of personalized FL approaches for ASR. Firstly, we adapt the personalization layer based FL for ASR, which keeps some layers locally to learn personalization models. Secondly, to reduce the communication and computation costs, we propose decoupled federated learning (DecoupleFL).

- How robust are pre-trained models to distribution shift? [[paper]](https://arxiv.org/abs/2206.08871)
  - Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H.S. Torr, Amartya Sanyal.
  - Key Word: Distribution Shifts; Self-Supervised Pre-Trainig.
  - <details><summary>Digest</summary> The vulnerability of machine learning models to spurious correlations has mostly been discussed in the context of supervised learning (SL). However, there is a lack of insight on how spurious correlations affect the performance of popular self-supervised learning (SSL) and auto-encoder based models (AE). In this work, we shed light on this by evaluating the performance of these models on both real world and synthetic distribution shift datasets. Following observations that the linear head itself can be susceptible to spurious correlations, we develop a novel evaluation scheme with the linear head trained on out-of-distribution (OOD) data, to isolate the performance of the pre-trained models from a potential bias of the linear head used for evaluation.

- Rectify ViT Shortcut Learning by Visual Saliency. [[paper]](https://arxiv.org/abs/2206.08567)
  - Chong Ma, Lin Zhao, Yuzhong Chen, David Weizhong Liu, Xi Jiang, Tuo Zhang, Xintao Hu, Dinggang Shen, Dajiang Zhu, Tianming Liu.
  - Key Word: Shortcut Learning; Vision Transformers; Eye Gaze Heatmap.
  - <details><summary>Digest</summary> We propose a novel and effective saliency-guided vision transformer (SGT) model to rectify shortcut learning in ViT with the absence of eye-gaze data. Specifically, a computational visual saliency model is adopted to predict saliency maps for input image samples. Then, the saliency maps are used to distil the most informative image patches. In the proposed SGT, the self-attention among image patches focus only on the distilled informative ones.

- GOOD: A Graph Out-of-Distribution Benchmark. [[paper]](https://arxiv.org/abs/2206.08452) [[code]](https://github.com/divelab/good)
  - Shurui Gui, Xiner Li, Limei Wang, Shuiwang Ji.
  - Key Word: Graph Neural Networks; Covariate Shifts; Concept Shifts.
  - <details><summary>Digest</summary> Currently, there lacks a systematic benchmark tailored to graph OOD method evaluation. In this work, we aim at developing an OOD benchmark, known as GOOD, for graphs specifically. We explicitly make distinctions between covariate and concept shifts and design data splits that accurately reflect different shifts. We consider both graph and node prediction tasks as there are key differences when designing shifts. Overall, GOOD contains 8 datasets with 14 domain selections. When combined with covariate, concept, and no shifts, we obtain 42 different splits. We provide performance results on 7 commonly used baseline methods with 10 random runs. This results in 294 dataset-model combinations in total.

- Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization. [[paper]](https://arxiv.org/abs/2206.07837)
  - Jivat Neet Kaur, Emre Kiciman, Amit Sharma.
  - Key Word: Out-of-Distribution Generalization; Multi-attribute Distribution Shifts; Causal Graph.
  - <details><summary>Digest</summary> Real-world data collected from multiple domains can have multiple, distinct distribution shifts over multiple attributes. However, state-of-the art advances in domain generalization (DG) algorithms focus only on specific shifts over a single attribute. We introduce datasets with multi-attribute distribution shifts and find that existing DG algorithms fail to generalize. To explain this, we use causal graphs to characterize the different types of shifts based on the relationship between spurious attributes and the classification label. Each multi-attribute causal graph entails different constraints over observed variables, and therefore any algorithm based on a single, fixed independence constraint cannot work well across all shifts. We present Causally Adaptive Constraint Minimization (CACM), a new algorithm for identifying the correct independence constraints for regularization.

- What makes domain generalization hard? [[paper]](https://arxiv.org/abs/2206.07802)
  - Spandan Madan, Li You, Mengmi Zhang, Hanspeter Pfister, Gabriel Kreiman.
  - Key Word: Domain Generalization; Scene Context.
  - <details><summary>Digest</summary> While several methodologies have been proposed for the daunting task of domain generalization, understanding what makes this task challenging has received little attention. Here we present SemanticDG (Semantic Domain Generalization): a benchmark with 15 photo-realistic domains with the same geometry, scene layout and camera parameters as the popular 3D ScanNet dataset, but with controlled domain shifts in lighting, materials, and viewpoints. Using this benchmark, we investigate the impact of each of these semantic shifts on generalization independently.

- Pareto Invariant Risk Minimization. [[paper]](https://arxiv.org/abs/2206.07766)
  - Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Kaili Ma, Yonggang Zhang, Han Yang, Bo Han, James Cheng.
  - Key Word: Invariant Learning; Multi-Task Learning.
  - <details><summary>Digest</summary> Despite the success of invariant risk minimization (IRM) in tackling the Out-of-Distribution generalization problem, IRM can compromise the optimality when applied in practice. The practical variants of IRM, e.g., IRMv1, have been shown to have significant gaps with IRM and thus could fail to capture the invariance even in simple problems. Moreover, the optimization procedure in IRMv1 involves two intrinsically conflicting objectives, and often requires careful tuning for the objective weights. To remedy the above issues, we reformulate IRM as a multi-objective optimization problem, and propose a new optimization scheme for IRM, called PAreto Invariant Risk Minimization (PAIR).

- Semantic-Discriminative Mixup for Generalizable Sensor-based Cross-domain Activity Recognition. [[paper]](http://arxiv.org/abs/2206.06629)
  - Wang Lu, Jindong Wang, Yiqiang Chen, Sinno Pan, Chunyu Hu, and Xin Qin. *ACM UbiComp 2022*
  - Key Word: Domain generalization; Activity recognition.
  - <details><summary>Digest</summary> We propose a novel method called Semantic-Discriminative Mixup (SDMix) for generalizable cross-domain HAR. Firstly, we introduce semantic-aware Mixup that considers the activity semantic ranges to overcome the semantic inconsistency brought by domain differences. Secondly, we introduce the large margin loss to enhance the discrimination of Mixup to prevent misclassification brought by noisy virtual labels.

- Invariant Structure Learning for Better Generalization and Causal Explainability. [[paper]](https://arxiv.org/abs/2206.06469)
  - Yunhao Ge, Sercan Ö. Arik, Jinsung Yoon, Ao Xu, Laurent Itti, Tomas Pfister.
  - Key Word: Causal Structure Discovery; Explainability; Invariant Learning.
  - <details><summary>Digest</summary> Learning the causal structure behind data is invaluable for improving generalization and obtaining high-quality explanations. We propose a novel framework, Invariant Structure Learning (ISL), that is designed to improve causal structure discovery by utilizing generalization as an indication. ISL splits the data into different environments, and learns a structure that is invariant to the target across different environments by imposing a consistency constraint. An aggregation mechanism then selects the optimal classifier based on a graph structure that reflects the causal mechanisms in the data more accurately compared to the structures learnt from individual environments.

- Causal Balancing for Domain Generalization. [[paper]](https://arxiv.org/abs/2206.05263)
  - Xinyi Wang, Michael Saxon, Jiachen Li, Hongyang Zhang, Kun Zhang, William Yang Wang.
  - Key Word: Invariant Learning; Causal Semantic Generative Model.
  - <details><summary>Digest</summary> While current domain generalization methods usually focus on enforcing certain invariance properties across different domains by new loss function designs, we propose a balanced mini-batch sampling strategy to reduce the domain-specific spurious correlations in the observed training distributions. More specifically, we propose a two-phased method that 1) identifies the source of spurious correlations, and 2) builds balanced mini-batches free from spurious correlations by matching on the identified source.

- GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing. [[paper]](https://arxiv.org/abs/2206.04310)
  - Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jun Zhu, Jian Song. *ICML 2022*
  - Key Word: Certified Semantic Robustness.
  - <details><summary>Digest</summary> Existing methods are insufficient or unable to provably defend against semantic transformations, especially those without closed-form expressions (such as defocus blur and pixelate), which are more common in practice and often unrestricted. To fill up this gap, we propose generalized randomized smoothing (GSmooth), a unified theoretical framework for certifying robustness against general semantic transformations via a novel dimension augmentation strategy. Under the GSmooth framework, we present a scalable algorithm that uses a surrogate image-to-image network to approximate the complex transformation.

- Sparse Fusion Mixture-of-Experts are Domain Generalizable Learners. [[paper]](https://arxiv.org/abs/2206.04046) [[code]](https://github.com/Luodian/SF-MoE-DG)
  - Bo Li, Jingkang Yang, Jiawei Ren, Yezhen Wang, Ziwei Liu.
  - Key Word: Domain Generalization; Vision Transformer; Sparse Mixture-of-Experts.
  - <details><summary>Digest</summary> We reveal the mixture-of-experts (MoE) model's generalizability on DG by leveraging to distributively handle multiple aspects of the predictive features across domains. To this end, we propose Sparse Fusion Mixture-of-Experts (SF-MoE), which incorporates sparsity and fusion mechanisms into the MoE framework to keep the model both sparse and predictive. SF-MoE has two dedicated modules: 1) sparse block and 2) fusion block, which disentangle and aggregate the diverse learned signals of an object, respectively.

- Toward Certified Robustness Against Real-World Distribution Shifts. [[paper]](https://arxiv.org/abs/2206.03669)
  - Haoze Wu, Teruhiro Tagomori, Alexander Robey, Fengjun Yang, Nikolai Matni, George Pappas, Hamed Hassani, Corina Pasareanu, Clark Barrett.
  - Key Word: Certified Robustness; Distribution Shift.
  - <details><summary>Digest</summary> We propose a general meta-algorithm for handling sigmoid activations which leverages classical notions of counter-example-guided abstraction refinement. The key idea is to "lazily" refine the abstraction of sigmoid functions to exclude spurious counter-examples found in the previous abstraction, thus guaranteeing progress in the verification process while keeping the state-space small.

- Can CNNs Be More Robust Than Transformers? [[paper]](https://arxiv.org/abs/2206.03452) [[code]](https://github.com/UCSC-VLAA/RobustCNN)
  - Zeyu Wang, Yutong Bai, Yuyin Zhou, Cihang Xie.
  - Key Word: Transformers; Distribution Shift.
  - <details><summary>Digest</summary> We question that belief by closely examining the design of Transformers. Our findings lead to three highly effective architecture designs for boosting robustness, yet simple enough to be implemented in several lines of code, namely a) patchifying input images, b) enlarging kernel size, and c) reducing activation layers and normalization layers.

- Distributionally Invariant Learning: Rationalization and Practical Algorithms. [[paper]](https://arxiv.org/abs/2206.02990)
  - Jiashuo Liu, Jiayun Wu, Jie Peng, Zheyan Shen, Bo Li, Peng Cui.
  - Key Word: Invariant Learning.
  - <details><summary>Digest</summary> We come up with the distributional invariance property as a relaxed alternative to the strict invariance, which considers the invariance only among sub-populations down to a prescribed scale and allows a certain degree of variation. We reformulate the invariant learning problem under latent heterogeneity into a relaxed form that pursues the distributional invariance, based on which we propose our novel Distributionally Invariant Learning (DIL) framework as well as two implementations named DIL-MMD and DIL-KL.

- Generalized Federated Learning via Sharpness Aware Minimization. [[paper]](https://arxiv.org/abs/2206.02618)
  - Zhe Qu, Xingyu Li, Rui Duan, Yao Liu, Bo Tang, Zhuo Lu. *ICML 2022*
  - Key Word: Personalized Federated Learning.
  - <details><summary>Digest</summary> We revisit the solutions to the distribution shift problem in FL with a focus on local learning generality. To this end, we propose a general, effective algorithm, FedSAM, based on Sharpness Aware Minimization (SAM) local optimizer, and develop a momentum FL algorithm to bridge local and global models, MoFedSAM. Theoretically, we show the convergence analysis of these two algorithms and demonstrate the generalization bound of FedSAM. Empirically, our proposed algorithms substantially outperform existing FL studies and significantly decrease the learning deviation.

- An Optimal Transport Approach to Personalized Federated Learning. [[paper]](https://arxiv.org/abs/2206.02468) [[code]](https://github.com/farzanfarnia/FedOT)
  - Farzan Farnia, Amirhossein Reisizadeh, Ramtin Pedarsani, Ali Jadbabaie.
  - Key Word: Personalized Federated Learning; Optimal Transport.
  - <details><summary>Digest</summary> We focus on this problem and propose a novel personalized Federated Learning scheme based on Optimal Transport (FedOT) as a learning algorithm that learns the optimal transport maps for transferring data points to a common distribution as well as the prediction model under the applied transport map. To formulate the FedOT problem, we extend the standard optimal transport task between two probability distributions to multi-marginal optimal transport problems with the goal of transporting samples from multiple distributions to a common probability domain. We then leverage the results on multi-marginal optimal transport problems to formulate FedOT as a min-max optimization problem and analyze its generalization and optimization properties.

- AugLoss: A Learning Methodology for Real-World Dataset Corruption. [[paper]](https://arxiv.org/abs/2206.02286)
  - Kyle Otstot, John Kevin Cava, Tyler Sypherd, Lalitha Sankar.
  - Key Word: Corruption Robustness; Data Augmentation.
  - <details><summary>Digest</summary> As a step towards addressing both problems simultaneously, we introduce AugLoss, a simple but effective methodology that achieves robustness against both train-time noisy labeling and test-time feature distribution shifts by unifying data augmentation and robust loss functions. We conduct comprehensive experiments in varied settings of real-world dataset corruption to showcase the gains achieved by AugLoss compared to previous state-of-the-art methods.

- Drawing out of Distribution with Neuro-Symbolic Generative Models. [[paper]](https://arxiv.org/abs/2206.01829)
  - Yichao Liang, Joshua B. Tenenbaum, Tuan Anh Le, N. Siddharth.
  - Key Word: Out-of-Distribution Generalization; Neuro-Symbolic Generative Models.
  - <details><summary>Digest</summary> Learning general-purpose representations from perceptual inputs is a hallmark of human intelligence. For example, people can write out numbers or characters, or even draw doodles, by characterizing these tasks as different instantiations of the same generic underlying process -- compositional arrangements of different forms of pen strokes. Crucially, learning to do one task, say writing, implies reasonable competence at another, say drawing, on account of this shared process. We present Drawing out of Distribution (DooD), a neuro-symbolic generative model of stroke-based drawing that can learn such general-purpose representations. In contrast to prior work, DooD operates directly on images, requires no supervision or expensive test-time inference, and performs unsupervised amortised inference with a symbolic stroke model that better enables both interpretability and generalization.

- On the Generalization of Wasserstein Robust Federated Learning. [[paper]](https://arxiv.org/abs/2206.01432)
  - Tung-Anh Nguyen, Tuan Dung Nguyen, Long Tan Le, Canh T. Dinh, Nguyen H. Tran.
  - Key Word: Wasserstein Distributionally Robust Optimization; Federated Learning.
  - <details><summary>Digest</summary> In federated learning, participating clients typically possess non-i.i.d. data, posing a significant challenge to generalization to unseen distributions. To address this, we propose a Wasserstein distributionally robust optimization scheme called WAFL. Leveraging its duality, we frame WAFL as an empirical surrogate risk minimization problem, and solve it using a local SGD-based algorithm with convergence guarantees. We show that the robustness of WAFL is more general than related approaches, and the generalization bound is robust to all adversarial distributions inside the Wasserstein ball (ambiguity set).

- Federated Learning under Distributed Concept Drift. [[paper]](https://arxiv.org/abs/2206.00799)
  - Ellango Jothimurugesan, Kevin Hsieh, Jianyu Wang, Gauri Joshi, Phillip B. Gibbons.
  - Key Word: Concept Drift; Federated Learning.
  - <details><summary>Digest</summary> Our work is the first to explicitly study data heterogeneity in both dimensions. We first demonstrate that prior solutions to drift adaptation, with their single global model, are ill-suited to staggered drifts, necessitating multi-model solutions. We identify the problem of drift adaptation as a time-varying clustering problem, and we propose two new clustering algorithms for reacting to drifts based on local drift detection and hierarchical clustering.

- Evolving Domain Generalization. [[paper]](https://arxiv.org/abs/2206.00047)
  - Wei Wang, Gezheng Xu, Ruizhi Pu, Jiaqi Li, Fan Zhou, Changjian Shui, Charles Ling, Christian Gagné, Boyu Wang.
  - Key Word: Domain Generalization.
  - <details><summary>Digest</summary> Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any target data. Existing domain generalization methods ignore the relationship between tasks, implicitly assuming that all the tasks are sampled from a stationary environment. Therefore, they can fail when deployed in an evolving environment. To this end, we formulate and study the \emph{evolving domain generalization} (EDG) scenario, which exploits not only the source data but also their evolving pattern to generate a model for the unseen task.

- Evaluating Robustness to Dataset Shift via Parametric Robustness Sets. [[paper]](https://arxiv.org/abs/2205.15947)
  - Nikolaj Thams, Michael Oberst, David Sontag.
  - Key Word: Distributionally Robust Optimization.
  - <details><summary>Digest</summary> We give a method for proactively identifying small, plausible shifts in distribution which lead to large differences in model performance. To ensure that these shifts are plausible, we parameterize them in terms of interpretable changes in causal mechanisms of observed variables. This defines a parametric robustness set of plausible distributions and a corresponding worst-case loss. While the loss under an individual parametric shift can be estimated via reweighting techniques such as importance sampling, the resulting worst-case optimization problem is non-convex, and the estimate may suffer from large variance.

- PAC Generalisation via Invariant Representations. [[paper]](https://arxiv.org/abs/2205.15196)
  - Advait Parulekar, Karthikeyan Shanmugam, Sanjay Shakkottai.
  - Key Word: Invariant Learning; Causal Structure Learning; Domain Adaptation.
  - <details><summary>Digest</summary> We study the following question: If a representation is approximately invariant with respect to a given number of training interventions, will it continue to be approximately invariant on a larger collection of unseen SEMs? This larger collection of SEMs is generated through a parameterized family of interventions. Inspired by PAC learning, we obtain finite-sample out-of-distribution generalization guarantees for approximate invariance that holds probabilistically over a family of linear SEMs without faithfulness assumptions.

- The Missing Invariance Principle Found -- the Reciprocal Twin of Invariant Risk Minimization. [[paper]](https://arxiv.org/abs/2205.14546)
  - Dongsung Huh, Avinash Baidya.
  - Key Word: Invariant Learning.
  - <details><summary>Digest</summary> We identify a fundamental flaw of IRM formulation that causes the failure. We then introduce a complementary notion of invariance, MRI, that is based on conserving the class-conditioned feature expectation across environments, that corrects for the flaw in IRM. Further, we introduce a simplified, practical version of the MRI formulation called as MRI-v1. We note that this constraint is convex which confers it with an advantage over the practical version of IRM, IRM-v1, which imposes non-convex constraints. We prove that in a general linear problem setting, MRI-v1 can guarantee invariant predictors given sufficient environments.

- Exploiting Adapters for Cross-lingual Low-resource Speech Recognition. [[paper]](https://arxiv.org/abs/2105.11905)
  - Wenxin Hou, Han Zhu, Yidong Wang, Jindong Wang, Tao Qin, Renjun Xu, and Takahiro Shinozaki. *TASLP 2022*
  - Key Word: Cross-domain learning; Speech recognition.
  - <details><summary>Digest</summary> We propose a novel algorithms called SimAdapter for explicitly learning knowledge from adapters. Our algorithm leverages adapters which can be easily integrated into the Transformer structure.MetaAdapter leverages meta-learning to transfer the general knowledge from training data to the test language. SimAdapter aims to learn the similarities between the source and target languages during fine-tuning using the adapters.

- FL Games: A federated learning framework for distribution shifts. [[paper]](https://arxiv.org/abs/2205.11101)
  - Sharut Gupta, Kartik Ahuja, Mohammad Havaei, Niladri Chatterjee, Yoshua Bengio.
  - Key Word: Distribution Shifts; Federated Learning.
  - <details><summary>Digest</summary> We argue that in order to generalize better across non-i.i.d. clients, it is imperative to only learn correlations that are stable and invariant across domains. We propose FL Games, a game-theoretic framework for federated learning for learning causal features that are invariant across clients. While training to achieve the Nash equilibrium, the traditional best response strategy suffers from high-frequency oscillations. We demonstrate that FL Games effectively resolves this challenge and exhibits smooth performance curves.

- Federated Learning Aggregation: New Robust Algorithms with Guarantees. [[paper]](https://arxiv.org/abs/2205.10864)
  - Adnan Ben Mansour, Gaia Carenini, Alexandre Duplessis, David Naccache.
  - Key Word: Federated Learning; Model Aggregation.
  - <details><summary>Digest</summary> We carry out a complete general mathematical convergence analysis to evaluate aggregation strategies in a federated learning framework. From this, we derive novel aggregation algorithms which are able to modify their model architecture by differentiating client contributions according to the value of their losses.

- Interpolating Compressed Parameter Subspaces. [[paper]](https://arxiv.org/abs/2205.09891)
  - Siddhartha Datta, Nigel Shadbolt.
  - Key Word: Distribution Shifts; Weight Averaging; Test-time distributions; Task interpolation.
  - <details><summary>Digest</summary> Inspired by recent work on neural subspaces and mode connectivity, we revisit parameter subspace sampling for shifted and/or interpolatable input distributions (instead of a single, unshifted distribution). We enforce a compressed geometric structure upon a set of trained parameters mapped to a set of train-time distributions, denoting the resulting subspaces as Compressed Parameter Subspaces (CPS). We show the success and failure modes of the types of shifted distributions whose optimal parameters reside in the CPS. We find that ensembling point-estimates within a CPS can yield a high average accuracy across a range of test-time distributions, including backdoor, adversarial, permutation, stylization and rotation perturbations. 

- Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification. [[paper]](https://arxiv.org/abs/2205.09619)
  - Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer, Doina Precup, Björn Eskofier, Dario Zanca. *ICML 2022*
  - Key Word: Decision Region Quantification; Corruption Robustness; Distribution Shift.
  - <details><summary>Digest</summary> We propose the Decision Region Quantification (DRQ) algorithm to improve the robustness of any differentiable pre-trained model against both real-world and worst-case distribution shifts in the data. DRQ analyzes the robustness of local decision regions in the vicinity of a given data point to make more reliable predictions. We theoretically motivate the DRQ algorithm by showing that it effectively smooths spurious local extrema in the decision surface.

- FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data. [[paper]](https://arxiv.org/abs/2205.09305) [[code]](https://github.com/mikemikezhu/FedILC)
  - Mike He Zhu, Léna Néhale Ezzine, Dianbo Liu, Yoshua Bengio.
  - Key Word: Regularization; Federated Learning.
  - <details><summary>Digest</summary> We propose the Federated Invariant Learning Consistency (FedILC) approach, which leverages the gradient covariance and the geometric mean of Hessians to capture both inter-silo and intra-silo consistencies of environments and unravel the domain shift problems in federated networks.

- Causality Inspired Representation Learning for Domain Generalization. [[paper]](https://arxiv.org/abs/2203.14237) [[code]](https://github.com/BIT-DA/CIRL)
  - Fangrui Lv, Jian Liang, Shuang Li, Bin Zang, Chi Harold Liu, Ziteng Wang, Di Liu. *CVPR 2022*
  - Key Word: Domain Generalization; Causality.
  - <details><summary>Digest</summary> We introduce a general structural causal model to formalize the DG problem. Specifically, we assume that each input is constructed from a mix of causal factors (whose relationship with the label is invariant across domains) and non-causal factors (category-independent), and only the former cause the classification judgments. Our goal is to extract the causal factors from inputs and then reconstruct the invariant causal mechanisms.

- Closing the Generalization Gap of Cross-silo Federated Medical Image Segmentation. [[paper]](https://arxiv.org/abs/2203.10144)
  - An Xu, Wenqi Li, Pengfei Guo, Dong Yang, Holger Roth, Ali Hatamizadeh, Can Zhao, Daguang Xu, Heng Huang, Ziyue Xu.
  - Key Word: Personalized Federated Learning; Medical Image Segmentation.
  - <details><summary>Digest</summary> We propose a novel training framework FedSM to avoid the client drift issue and successfully close the generalization gap compared with the centralized training for medical image segmentation tasks for the first time. We also propose a novel personalized FL objective formulation and a new method SoftPull to solve it in our proposed framework FedSM.

- Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. [[paper]](https://arxiv.org/abs/2203.05482) [[code]](https://github.com/mlfoundations/model-soups)
  - Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S. Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, Ludwig Schmidt. *ICML 2022*
  - Key Word: Wegiht Averaging; Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> We show that averaging the weights of multiple models fine-tuned with different hyperparameter configurations often improves accuracy and robustness. Unlike a conventional ensemble, we may average many models without incurring any additional inference or memory costs -- we call the results "model soups." When fine-tuning large pre-trained models such as CLIP, ALIGN, and a ViT-G pre-trained on JFT, our soup recipe provides significant improvements over the best model in a hyperparameter sweep on ImageNet.

- Continual Feature Selection: Spurious Features in Continual Learning. [[paper]](https://arxiv.org/abs/2203.01012)
  - Timothée Lesort.
  - Key Word: Spurious Correlations; Continual Learning.
  - <details><summary>Digest</summary> This paper studies spurious features' influence on continual learning algorithms. We show that continual learning algorithms solve tasks by selecting features that are not generalizable. Our experiments highlight that continual learning algorithms face two related problems: (1) spurious features and (2) local spurious features. The first one is due to a covariate shift between training and testing data, while the second is due to the limited access to data at each training step. We study (1) through a consistent set of continual learning experiments varying spurious correlation amount and data distribution support. We show that (2) is a major cause of performance decrease in continual learning along with catastrophic forgetting. 

- Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution. [[paper]](https://arxiv.org/abs/2202.10054) [[code]](https://github.com/AnanyaKumar/transfer_learning)
  - Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, Percy Liang.
  - Key Word: Fine-Tuning; Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> We show theoretically that this tradeoff between ID and OOD accuracy arises even in a simple setting: fine-tuning overparameterized two-layer linear networks. We prove that the OOD error of fine-tuning is high when we initialize with a fixed or random head -- this is because while fine-tuning learns the head, the lower layers of the neural network change simultaneously and distort the pretrained features. Our analysis suggests that the easy two-step strategy of linear probing then full fine-tuning (LP-FT), sometimes used as a fine-tuning heuristic, combines the benefits of both fine-tuning and linear probing. 

- Uncertainty Modeling for Out-of-Distribution Generalization. [[paper]](https://arxiv.org/abs/2202.03958) [[code]](https://github.com/lixiaotong97/dsu)
  - Xiaotong Li, Yongxing Dai, Yixiao Ge, Jun Liu, Ying Shan, Ling-Yu Duan. *ICLR 2022*
  - Key Word: Out-of-Distribution Generalization; Uncertainty.
  - <details><summary>Digest</summary> We improve the network generalization ability by modeling the uncertainty of domain shifts with synthesized feature statistics during training. Specifically, we hypothesize that the feature statistic, after considering the potential uncertainties, follows a multivariate Gaussian distribution. Hence, each feature statistic is no longer a deterministic value, but a probabilistic point with diverse distribution possibilities. With the uncertain feature statistics, the models can be trained to alleviate the domain perturbations and achieve better robustness against potential domain shifts.

- Benchmarking and Analyzing Point Cloud Classification under Corruptions. [[paper]](https://arxiv.org/abs/2202.03377) [[code]](https://github.com/jiawei-ren/modelnetc)
  - Jiawei Ren, Liang Pan, Ziwei Liu. *ICML 2022*
  - Key Word: Corruption Robustness; Point Cloud Classification; Benchmarks.
  - <details><summary>Digest</summary> 3D perception, especially point cloud classification, has achieved substantial progress. However, in real-world deployment, point cloud corruptions are inevitable due to the scene complexity, sensor inaccuracy, and processing imprecision. In this work, we aim to rigorously benchmark and analyze point cloud classification under corruptions. To conduct a systematic investigation, we first provide a taxonomy of common 3D corruptions and identify the atomic corruptions. Then, we perform a comprehensive evaluation on a wide range of representative point cloud models to understand their robustness and generalizability.

- Handling Distribution Shifts on Graphs: An Invariance Perspective. [[paper]](https://arxiv.org/abs/2202.02466) [[code]](https://github.com/qitianwu/graphood-eerm)
  - Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf. *ICLR 2022*
  - Key Word: Distribution Shifts; Graph Neural Networks.
  - <details><summary>Digest</summary> We formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM), that facilitates graph neural networks to leverage invariance principles for prediction. EERM resorts to multiple context explorers (specified as graph structure editers in our case) that are adversarially trained to maximize the variance of risks from multiple virtual environments.

- Certifying Out-of-Domain Generalization for Blackbox Functions. [[paper]](https://arxiv.org/abs/2202.01679)
  - Maurice Weber, Linyi Li, Boxin Wang, Zhikuan Zhao, Bo Li, Ce Zhang.
  - Key Word: Certified Distributional Robustness; Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> We focus on the problem of certifying distributional robustness for black box models and bounded losses, without other assumptions. We propose a novel certification framework given bounded distance of mean and variance of two distributions. Our certification technique scales to ImageNet-scale datasets, complex models, and a diverse range of loss functions. We then focus on one specific application enabled by such scalability and flexibility, i.e., certifying out-of-domain generalization for large neural networks and loss functions such as accuracy and AUC.

- Provable Domain Generalization via Invariant-Feature Subspace Recovery. [[paper]](https://arxiv.org/abs/2201.12919) [[code]](https://github.com/haoxiang-wang/isr)  
  - Haoxiang Wang, Haozhe Si, Bo Li, Han Zhao. *ICML 2022*
  - Key Word: Domain Generalization; Invariant Learning.
  - <details><summary>Digest</summary> we propose to achieve domain generalization with Invariant-feature Subspace Recovery (ISR). Our first algorithm, ISR-Mean, can identify the subspace spanned by invariant features from the first-order moments of the class-conditional distributions, and achieve provable domain generalization with ds+1 training environments under the data model of Rosenfeld et al. (2021). Our second algorithm, ISR-Cov, further reduces the required number of training environments to O(1) using the information of second-order moments.

- Certifying Model Accuracy under Distribution Shifts. [[paper]](https://arxiv.org/abs/2201.12440)
  - Aounon Kumar, Alexander Levine, Tom Goldstein, Soheil Feizi.
  - Key Word: Certified Distributional Robustness; Corruption Robustness.
  - <details><summary>Digest</summary> Certified robustness in machine learning has primarily focused on adversarial perturbations of the input with a fixed attack budget for each point in the data distribution. In this work, we present provable robustness guarantees on the accuracy of a model under bounded Wasserstein shifts of the data distribution. We show that a simple procedure that randomizes the input of the model within a transformation space is provably robust to distributional shifts under the transformation. Our framework allows the datum-specific perturbation size to vary across different points in the input distribution and is general enough to include fixed-sized perturbations as well.

</details>

<!-- ### Out-of-Distribution Generalization: 2021 -->

<details>
<summary>Out-of-Distribution Generalization: 2021</summary>

- Margin Calibration for Long-Tailed Visual Recognition. [[paper]](https://arxiv.org/abs/2112.07225)
  - Yidong Wang, Bowen Zhang, Wenxin Hou, Zhen Wu, Jindong Wang, and Takahiro Shinozaki. *ACML 2022*
  - Key Word: long-tailed recognition; imbalance learning
  - <details><summary>Digest</summary> We study the relationship between the margins and logits (classification scores) and empirically observe the biased margins and the biased logits are positively correlated. We propose MARC, a simple yet effective MARgin Calibration function to dynamically calibrate the biased margins for unbiased logits. MARC is extremely easy: just three lines of code.

- PixMix: Dreamlike Pictures Comprehensively Improve Safety Measures. [[paper]](https://arxiv.org/abs/2112.05135) [[code]](https://github.com/andyzoujm/pixmix)
  - Dan Hendrycks, Andy Zou, Mantas Mazeika, Leonard Tang, Bo Li, Dawn Song, Jacob Steinhardt. *CVPR 2022*
  - Key Word: Corruption Robustness; Data Augmentation; Calibration; Anomaly Detection.
  - <details><summary>Digest</summary> In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy. These other goals include out-of-distribution (OOD) robustness, prediction consistency, resilience to adversaries, calibrated uncertainty estimates, and the ability to detect anomalous inputs. However, improving performance towards these goals is often a balancing act that today's methods cannot achieve without sacrificing performance on other safety axes. For instance, adversarial training improves adversarial robustness but sharply degrades other classifier performance metrics. Similarly, strong data augmentation and regularization techniques often improve OOD robustness but harm anomaly detection, raising the question of whether a Pareto improvement on all existing safety measures is possible. To meet this challenge, we design a new data augmentation strategy utilizing the natural structural complexity of pictures such as fractals, which outperforms numerous baselines, is near Pareto-optimal, and roundly improves safety measures.

- Certified Adversarial Defenses Meet Out-of-Distribution Corruptions: Benchmarking Robustness and Simple Baselines. [[paper]](https://arxiv.org/abs/2112.00659)
  - Jiachen Sun, Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Dan Hendrycks, Jihun Hamm, Z. Morley Mao. *ECCV 2022*
  - Key Word: Corruption Robustness; Certified Adversarial Robustness.
  - <details><summary>Digest</summary> Certified robustness guarantee gauges a model's robustness to test-time attacks and can assess the model's readiness for deployment in the real world. In this work, we critically examine how the adversarial robustness guarantees from randomized smoothing-based certification methods change when state-of-the-art certifiably robust models encounter out-of-distribution (OOD) data. Our analysis demonstrates a previously unknown vulnerability of these models to low-frequency OOD data such as weather-related corruptions, rendering these models unfit for deployment in the wild.

- Failure Modes of Domain Generalization Algorithms. [[paper]](https://arxiv.org/abs/2111.13733) [[code]](https://github.com/YerevaNN/dom-gen-failure-modes)  
  - Tigran Galstyan, Hrayr Harutyunyan, Hrant Khachatrian, Greg Ver Steeg, Aram Galstyan. *CVPR 2022*
  - Key Word: Domain Generalization; Invariant Learning.
  - <details><summary>Digest</summary> We propose an evaluation framework for domain generalization algorithms that allows decomposition of the error into components capturing distinct aspects of generalization. Inspired by the prevalence of algorithms based on the idea of domain-invariant representation learning, we extend the evaluation framework to capture various types of failures in achieving invariance. We show that the largest contributor to the generalization error varies across methods, datasets, regularization strengths and even training lengths.

- Why Stable Learning Works? A Theory of Covariate Shift Generalization. [[paper]](https://arxiv.org/abs/2111.02355)
  - Renzhe Xu, Peng Cui, Zheyan Shen, Xingxuan Zhang, Tong Zhang.
  - Key Word: Stable Learning.
  - <details><summary>Digest</summary> Covariate shift generalization, a typical case in out-of-distribution (OOD) generalization, requires a good performance on the unknown testing distribution, which varies from the accessible training distribution in the form of covariate shift. Recently, stable learning algorithms have shown empirical effectiveness to deal with covariate shift generalization on several learning models involving regression algorithms and deep neural networks. However, the theoretical explanations for such effectiveness are still missing. In this paper, we take a step further towards the theoretical analysis of stable learning algorithms by explaining them as feature selection processes.

- Benchmarks for Corruption Invariant Person Re-identification. [[paper]](https://arxiv.org/abs/2111.00880) [[code]](https://github.com/MinghuiChen43/CIL-ReID)
  - Minghui Chen, Zhiqiang Wang, Feng Zheng. *NeurIPS 2021*
  - Key Word: Corruption Robustness; Benchmark; Person Re-Identificaiton.
  - <details><summary>Digest</summary> We comprehensively establish six ReID benchmarks for learning corruption invariant representation. In the field of ReID, we are the first to conduct an exhaustive study on corruption invariant learning in single- and cross-modality datasets, including Market-1501, CUHK03, MSMT17, RegDB, SYSU-MM01. After reproducing and examining the robustness performance of 21 recent ReID methods, we have some observations: 1) transformer-based models are more robust towards corrupted images, compared with CNN-based models, 2) increasing the probability of random erasing (a commonly used augmentation method) hurts model corruption robustness, 3) cross-dataset generalization improves with corruption robustness increases. By analyzing the above observations, we propose a strong baseline on both single- and cross-modality ReID datasets which achieves improved robustness against diverse corruptions.

- Kernelized Heterogeneous Risk Minimization. [[paper]](https://arxiv.org/abs/2110.12425) [[code]](https://github.com/LJSthu/Kernelized-HRM)
  - Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen. *NeurIPS 2021*
  - Key Word: Invariant Learning; Neural Tangent Kernel.
  - <details><summary>Digest</summary> We propose Kernelized Heterogeneous Risk Minimization (KerHRM) algorithm, which achieves both the latent heterogeneity exploration and invariant learning in kernel space, and then gives feedback to the original neural network by appointing invariant gradient direction. We theoretically justify our algorithm and empirically validate the effectiveness of our algorithm with extensive experiments.

- A Fine-Grained Analysis on Distribution Shift. [[paper]](https://arxiv.org/abs/2110.11328)  
  - Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre Alvise-Rebuffi, Ira Ktena, Krishnamurthy Dvijotham, Taylan Cemgil. *ICLR 2022*
  - Key Word: Distribution Shifts; Data Augmentation.
  - <details><summary>Digest</summary> Robustness to distribution shifts is critical for deploying machine learning models in the real world. Despite this necessity, there has been little work in defining the underlying mechanisms that cause these shifts and evaluating the robustness of algorithms across multiple, different distribution shifts. To this end, we introduce a framework that enables fine-grained analysis of various distribution shifts. We provide a holistic analysis of current state-of-the-art methods by evaluating 19 distinct methods grouped into five categories across both synthetic and real-world datasets. Overall, we train more than 85K models. Our experimental framework can be easily extended to include new methods, shifts, and datasets. We find, unlike previous work, that progress has been made over a standard ERM baseline; in particular, pretraining and augmentations (learned or heuristic) offer large gains in many cases. However, the best methods are not consistent over different datasets and shifts.

- Benchmarking the Robustness of Spatial-Temporal Models Against Corruptions. [[paper]](https://arxiv.org/abs/2110.06513) [[code]](https://github.com/newbeeyoung/video-corruption-robustness)
  - Chenyu Yi, SIYUAN YANG, Haoliang Li, Yap-peng Tan, Alex Kot. *NeurIPS 2021*
  - Key Word: Video Understanding; Corruption Robustness.
  - <details><summary>Digest</summary> We establish a corruption robustness benchmark, Mini Kinetics-C and Mini SSV2-C, which considers temporal corruptions beyond spatial corruptions in images. We make the first attempt to conduct an exhaustive study on the corruption robustness of established CNN-based and Transformer-based spatial-temporal models.

- Towards Out-Of-Distribution Generalization: A Survey. [[paper]](https://arxiv.org/abs/2108.13624)
  - Zheyan Shen, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, Peng Cui.
  - Key Word: Survey; Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> Out-of-Distribution (OOD) generalization problem addresses the challenging setting where the testing distribution is unknown and different from the training. This paper serves as the first effort to systematically and comprehensively discuss the OOD generalization problem, from the definition, methodology, evaluation to the implications and future directions.

- Learning to Diversify for Single Domain Generalization. [[paper]](https://arxiv.org/abs/2108.11726) [[code]](https://github.com/busername/learning_to_diversify)
  - Zijian Wang, Yadan Luo, Ruihong Qiu, Zi Huang, Mahsa Baktashmotlagh. *ICCV 2021*
  - Key Word: Corruption Robustness; Single Domain Generalization.
  - <details><summary>Digest</summary> To tackle this problem, we propose a style-complement module to enhance the generalization power of the model by synthesizing images from diverse distributions that are complementary to the source ones. More specifically, we adopt a tractable upper bound of mutual information (MI) between the generated and source samples and perform a two-step optimization iteratively.

- StyleAugment: Learning Texture De-biased Representations by Style Augmentation without Pre-defined Textures. [[paper]](https://arxiv.org/abs/2108.10549)
  - Sanghyuk Chun, Song Park.
  - Key Word: Corruption Robustness; Shape-Texture; Style Transfer; Data Augmentation.
  - <details><summary>Digest</summary> Stylized ImageNet approach has two drawbacks in fidelity and diversity. First, the generated images show low image quality due to the significant semantic gap betweeen natural images and artistic paintings. Also, Stylized ImageNet training samples are pre-computed before training, resulting in showing the lack of diversity for each sample. We propose a StyleAugment by augmenting styles from the mini-batch.

- Adarnn: Adaptive learning and forecasting of time series. [[paper]](https://arxiv.org/abs/2108.04443)
  - Yuntao Du, Jindong Wang, Wenjie Feng, Sinno Pan, Tao Qin, Renjun Xu, and Chongjun Wang. *CIKM 2021*
  - Key Word: Out-of-distribution prediction; Time series analysis
  - <details><summary>Digest</summary> This paper proposes Adaptive RNNs (AdaRNN) to tackle the TCS problem by building an adaptive model that generalizes well on the unseen test data. AdaRNN is sequentially composed of two novel algorithms. First, we propose Temporal Distribution Characterization to better characterize the distribution information in the TS. Second, we propose Temporal Distribution Matching to reduce the distribution mismatch in TS to learn the adaptive TS model. AdaRNN is a general framework with flexible distribution distances integrated.

- Impact of Aliasing on Generalization in Deep Convolutional Networks. [[paper]](https://arxiv.org/abs/2108.03489)
  - Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Rob Romijnders, Nicolas Le Roux, Ross Goroshin. *ICCV 2021*
  - Key Word: Corruption Robustness; Anti-Aliasing.
  - <details><summary>Digest</summary> Drawing insights from frequency analysis theory, we take a closer look at ResNet and EfficientNet architectures and review the trade-off between aliasing and information loss in each of their major components. We show how to mitigate aliasing by inserting non-trainable low-pass filters at key locations, particularly where networks lack the capacity to learn them.

- Using Synthetic Corruptions to Measure Robustness to Natural Distribution Shifts. [[paper]](https://arxiv.org/abs/2107.12052)
  - Alfred Laugros, Alice Caplier, Matthieu Ospici.
  - Key Word: Corruption Robustness; Benchmark.
  - <details><summary>Digest</summary> We propose a methodology to build synthetic corruption benchmarks that make robustness estimations more correlated with robustness to real-world distribution shifts. Using the overlapping criterion, we split synthetic corruptions into categories that help to better understand neural network robustness.

- Just Train Twice: Improving Group Robustness without Training Group Information. [[paper]](https://arxiv.org/abs/2107.09044) [[code]](https://github.com/anniesch/jtt)
  - Evan Zheran Liu, Behzad Haghgoo, Annie S. Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn. *ICML 2021*
  - Key Word: Invariant Learning; Robust Optimization; Fairness.
  - <details><summary>Digest</summary> We propose a simple two-stage approach, JTT, that first trains a standard ERM model for several epochs, and then trains a second model that upweights the training examples that the first model misclassified. Intuitively, this upweights examples from groups on which standard ERM models perform poorly, leading to improved worst-group performance.

- Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. [[paper]](https://arxiv.org/abs/2107.08221) [[code]](https://github.com/bethgelab/InDomainGeneralizationBenchmark)
  - Lukas Schott, Julius von Kügelgen, Frederik Träuble, Peter Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel. *ICLR 2022*
  - Key Word: Out-of-Distribution Generalization; Disentanglement; Benchmark.
  - <details><summary>Digest</summary> In contrast to prior robustness work that introduces novel factors of variation during test time, such as blur or other (un)structured noise, we here recompose, interpolate, or extrapolate only existing factors of variation from the training data set (e.g., small and medium-sized objects during training and large objects during testing). Models that learn the correct mechanism should be able to generalize to this benchmark. In total, we train and test 2000+ models and observe that all of them struggle to learn the underlying mechanism regardless of supervision signal and architectural bias. Moreover, the generalization capabilities of all tested models drop significantly as we move from artificial datasets towards more realistic real-world datasets.

- Global Filter Networks for Image Classification. [[paper]](https://arxiv.org/abs/2107.00645) [[code]](https://github.com/raoyongming/GFNet)
  - Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie Zhou.
  - Key Word: Corruption Robustness; Frequency.
  - <details><summary>Digest</summary> We present the Global Filter Network (GFNet), a conceptually simple yet computationally efficient architecture, that learns long-term spatial dependencies in the frequency domain with log-linear complexity.

- The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning. [[paper]](https://arxiv.org/abs/2106.15831)
  - Anders Andreassen, Yasaman Bahri, Behnam Neyshabur, Rebecca Roelofs.
  - Key Word: Out-of-Distribution Generalization; Fine-Tuning.
  - <details><summary>Digest</summary> Although machine learning models typically experience a drop in performance on out-of-distribution data, accuracies on in- versus out-of-distribution data are widely observed to follow a single linear trend when evaluated across a testbed of models. Models that are more accurate on the out-of-distribution data relative to this baseline exhibit "effective robustness" and are exceedingly rare. Identifying such models, and understanding their properties, is key to improving out-of-distribution performance. We conduct a thorough empirical investigation of effective robustness during fine-tuning and surprisingly find that models pre-trained on larger datasets exhibit effective robustness during training that vanishes at convergence. We study how properties of the data influence effective robustness, and we show that it increases with the larger size, more diversity, and higher example difficulty of the dataset. 

- Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation. [[paper]](https://arxiv.org/abs/2106.14999)
  - Chaithanya Kumar Mummadi, Robin Hutmacher, Kilian Rambach, Evgeny Levinkov, Thomas Brox, Jan Hendrik Metzen.
  - Key Word: Corruption Robustness; Test-Time Adaptation.
  - <details><summary>Digest</summary> We propose non-saturating losses based on the negative log likelihood ratio, such that gradients from high confidence predictions still contribute to test-time adaptation.

- Exploring Corruption Robustness: Inductive Biases in Vision Transformers and MLP-Mixers. [[paper]](https://arxiv.org/abs/2106.13122) [[code]](https://github.com/katelyn98/CorruptionRobustness)
  - Katelyn Morrison, Benjamin Gilby, Colton Lipchak, Adam Mattioli, Adriana Kovashka.
  - Key Word: Corruption Robustness; Transformers.
  - <details><summary>Digest</summary> We find that vision transformer architectures are inherently more robust to corruptions than the ResNet and MLP-Mixers.

- Dangers of Bayesian Model Averaging under Covariate Shift. [[paper]](https://arxiv.org/abs/2106.11905)
  - Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson.
  - Key Word: Corruption Robustness; Bayesian Neural Networks.
  - <details><summary>Digest</summary> Bayesian neural networks (BNNs) with high-fidelity approximate inference via full-batch Hamiltonian Monte Carlo achieve poor generalization under covariate shift, even underperforming classical estimation. We explain this surprising result, showing how a Bayesian model average can in fact be problematic under covariate shift, particularly in cases where linear dependencies in the input features cause a lack of posterior contraction.

- A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness. [[paper]](https://arxiv.org/abs/2106.09129)
  - James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura.
  - Key Word: Corruption Robustness; Lottery Ticket Hypothesis.
  - <details><summary>Digest</summary> We present the first positive result on simultaneously achieving high accuracy and OoD robustness at extreme levels of model compression.

- Delving Deep into the Generalization of Vision Transformers under Distribution Shifts. [[paper]](https://arxiv.org/abs/2106.07617) [[code]](https://github.com/Phoenix1153/ViT_OOD_generalization)
  - Chongzhi Zhang, Mingyuan Zhang, Shanghang Zhang, Daisheng Jin, Qiang Zhou, Zhongang Cai, Haiyu Zhao, Shuai Yi, Xianglong Liu, Ziwei Liu.
  - Key Word: Corruption Robustness; Transformers.
  - <details><summary>Digest</summary> We first present a taxonomy of distribution shifts by categorizing them into five conceptual groups: corruption shift, background shift, texture shift, destruction shift, and style shift. Then we perform extensive evaluations of ViT variants under different groups of distribution shifts and compare their generalization ability with Convolutional Neural Network (CNN) models.

- RobustNav: Towards Benchmarking Robustness in Embodied Navigation. [[paper]](https://arxiv.org/abs/2106.04531) [[code]](https://github.com/allenai/robustnav)
  - Prithvijit Chattopadhyay, Judy Hoffman, Roozbeh Mottaghi, Aniruddha Kembhavi. *ICCV 2021*
  - Key Word: Corruption Robustness; Embodied Navigation.
  - <details><summary>Digest</summary> As an attempt towards assessing the robustness of embodied navigation agents, we propose RobustNav, a framework to quantify the performance of embodied navigation agents when exposed to a wide variety of visual - affecting RGB inputs - and dynamics - affecting transition dynamics - corruptions. Most recent efforts in visual navigation have typically focused on generalizing to novel target environments with similar appearance and dynamics characteristics. With RobustNav, we find that some standard embodied navigation agents significantly underperform (or fail) in the presence of visual or dynamics corruptions.

- Towards a Theoretical Framework of Out-of-Distribution Generalization. [[paper]](https://arxiv.org/abs/2106.04496)
  - Haotian Ye, Chuanlong Xie, Tianle Cai, Ruichen Li, Zhenguo Li, Liwei Wang.
  - Key Word: Theoretical Framework; Out-of-Distribution Generalization.
  - <details><summary>Digest</summary> We take the first step towards rigorous and quantitative definitions of 1) what is OOD; and 2) what does it mean by saying an OOD problem is learnable. We also introduce a new concept of expansion function, which characterizes to what extent the variance is amplified in the test domains over the training domains, and therefore give a quantitative meaning of invariant features.

- An Information-theoretic Approach to Distribution Shifts. [[paper]](https://arxiv.org/abs/2106.03783) [[code]](https://github.com/mfederici/dsit)
  - Marco Federici, Ryota Tomioka, Patrick Forré. *NeurIPS 2021*
  - Key Word: Information Theory; Distribution Shift.
  - <details><summary>Digest</summary> We describe the problem of data shift from a novel information-theoretic perspective by (i) identifying and describing the different sources of error, (ii) comparing some of the most promising objectives explored in the recent domain generalization, and fair classification literature.

- OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization. [[paper]](https://arxiv.org/abs/2106.03721) [[code]](https://github.com/ynysjtu/ood_bench)
  - Nanyang Ye, Kaican Li, Haoyue Bai, Runpeng Yu, Lanqing Hong, Fengwei Zhou, Zhenguo Li, Jun Zhu. *CVPR 2022*
  - Key Word: Out-of-Distribution Generalization; Benchmark.
  - <details><summary>Digest</summary> We first identify and measure two distinct kinds of distribution shifts that are ubiquitous in various datasets. Next, through extensive experiments, we compare OoD generalization algorithms across two groups of benchmarks, each dominated by one of the distribution shifts, revealing their strengths on one shift as well as limitations on the other shift.

- Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers. [[paper]](https://arxiv.org/abs/2105.12628) [[code]](https://github.com/YujiaBao/Predict-then-Interpolate)
  - Yujia Bao, Shiyu Chang, Regina Barzilay. *ICML 2022*
  - Key Word: Invariant Learning; Fairness.
  - <details><summary>Digest</summary> We prove that by interpolating the distributions of the correct predictions and the wrong predictions, we can uncover an oracle distribution where the unstable correlation vanishes. Since the oracle interpolation coefficients are not accessible, we use group distributionally robust optimization to minimize the worst-case risk across all such interpolations.

- Using the Overlapping Score to Improve Corruption Benchmarks. [[paper]](https://arxiv.org/abs/2105.12357)
  - Alfred Laugros, Alice Caplier, Matthieu Ospici.
  - Key Word: Corruption Robustness; Benchmark.
  - <details><summary>Digest</summary> We propose a metric called corruption overlapping score, which can be used to reveal flaws in corruption benchmarks. Two corruptions overlap when the robustnesses of neural networks to these corruptions are correlated.

- Improved OOD Generalization via Adversarial Training and Pre-training. [[paper]](https://arxiv.org/abs/2105.11144)
  - Mingyang Yi, Lu Hou, Jiacheng Sun, Lifeng Shang, Xin Jiang, Qun Liu, Zhi-Ming Ma. *ICML 2021*
  - Key Word: Corruption Robustness; Adversarial Training; Pre-Trainig.
  - <details><summary>Digest</summary> In this paper, after defining OOD generalization via Wasserstein distance, we theoretically show that a model robust to input perturbation generalizes well on OOD data. Inspired by previous findings that adversarial training helps improve input-robustness, we theoretically show that adversarially trained models have converged excess risk on OOD data, and empirically verify it on both image classification and natural language understanding tasks.

- Balancing Robustness and Sensitivity using Feature Contrastive Learning. [[paper]](https://arxiv.org/abs/2105.09394)
  - Seungyeon Kim, Daniel Glasner, Srikumar Ramalingam, Cho-Jui Hsieh, Kishore Papineni, Sanjiv Kumar.
  - Key Word: Corruption Robustness.
  - <details><summary>Digest</summary> We discuss this trade-off between sensitivity and robustness to natural (non-adversarial) perturbations by introducing two notions: contextual feature utility and contextual feature sensitivity. We propose Feature Contrastive Learning (FCL) that encourages a model to be more sensitive to the features that have higher contextual utility.

- Towards Robust Vision Transformer. [[paper]](https://arxiv.org/abs/2105.07926) [[code]](https://github.com/vtddggg/Robust-Vision-Transformer)
  - Xiaofeng Mao, Gege Qi, Yuefeng Chen, Xiaodan Li, Ranjie Duan, Shaokai Ye, Yuan He, Hui Xue.
  - Key Word: Corruption Robustness; Transformers.
  - <details><summary>Digest</summary> Wwe propose a Robust Vision Transformer (RVT), by using and combining robust components as building blocks.To further improve the RVT, we propose two new plug-and-play techniques called position-aware attention scaling and patch-wise augmentation.

- Vision Transformers are Robust Learners. [[paper]](https://arxiv.org/abs/2105.07581) [[code]](https://github.com/sayakpaul/robustness-vit)
  - Sayak Paul, Pin-Yu Chen.
  - Key Word: Corruption Robustness; Transformers.
  - <details><summary>Digest</summary> We study the robustness of the Vision Transformer (ViT) against common corruptions and perturbations, distribution shifts, and natural adversarial examples.

- When Human Pose Estimation Meets Robustness: Adversarial Algorithms and Benchmarks. [[paper]](https://arxiv.org/abs/2105.06152) [[code]](https://github.com/AIprogrammer/AdvMix)
  - Jiahang Wang, Sheng Jin, Wentao Liu, Weizhong Liu, Chen Qian, Ping Luo. *CVPR 2021*
  - Key Word: Corruption Robustness; Benchmark; Data Augmentation; Pose Estimation.
  - <details><summary>Digest</summary> This work comprehensively studies and addresses this problem by building rigorous robust benchmarks, termed COCO-C, MPII-C, and OCHuman-C, to evaluate the weaknesses of current advanced pose estimators, and a new algorithm termed AdvMix is proposed to improve their robustness in different corruptions.

- FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis. [[paper]](https://arxiv.org/abs/2105.05001)
  - Baihe Huang, Xiaoxiao Li, Zhao Song, Xin Yang. *ICML 2021*
  - Keyword: Federated Learning; Neural Tangent Kernel.
  - <details><summary>Digest</summary> This paper presents a new class of convergence analysis for FL, Federated Learning Neural Tangent Kernel (FL-NTK), which corresponds to overparamterized ReLU neural networks trained by gradient descent in FL and is inspired by the analysis in Neural Tangent Kernel (NTK). Theoretically, FL-NTK converges to a global-optimal solution at a linear rate with properly tuned learning parameters.

- Heterogeneous Risk Minimization. [[paper]](https://arxiv.org/abs/2105.03818)
  - Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen. *ICML 2021*
  - Key Word: Invariant Learning; Causality; Robust Optimization.
  - <details><summary>Digest</summary> We propose Heterogeneous Risk Minimization (HRM) framework to achieve joint learning of latent heterogeneity among the data and invariant relationship, which leads to stable prediction despite distributional shifts. We theoretically characterize the roles of the environment labels in invariant learning and justify our newly proposed HRM framework.

- What Are Bayesian Neural Network Posteriors Really Like? [[paper]](https://arxiv.org/abs/2104.14421) [[code]](https://github.com/google-research/google-research/tree/master/bnn_hmc)
  - Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson.
  - Key Word: Corruption Robustness; Bayesian Neural Networks.
  - <details><summary>Digest</summary> Bayesian neural networks show surprisingly poor generalization under domain shift; while cheaper alternatives such as deep ensembles and SGMCMC methods can provide good generalization, they provide distinct predictive distributions from HMC.

- Adapting ImageNet-scale models to complex distribution shifts with self-learning. [[paper]](https://arxiv.org/abs/2104.12928) [[code]](https://domainadaptation.org/selflearning/)
  - Evgenia Rusak, Steffen Schneider, Peter Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge.
  - Key Word: Corruption Robustness; Domain Adaptation.
  - <details><summary>Digest</summary> We find that three components are crucial for increasing performance with self-learning: (i) using short update times between the teacher and the student network, (ii) fine-tuning only few affine parameters distributed across the network, and (iii) leveraging methods from robust classification to counteract the effect of label noise. We therefore re-purpose the dataset from the Visual Domain Adaptation Challenge 2019 and use a subset of it as a new robustness benchmark (ImageNet-D) which proves to be a more challenging dataset.

- Towards Corruption-Agnostic Robust Domain Adaptation. [[paper]](https://arxiv.org/abs/2104.10376) [[code]](https://github.com/Mike9674/CRDA)
  - Yifan Xu, Kekai Sheng, Weiming Dong, Baoyuan Wu, Changsheng Xu, Bao-Gang Hu.
  - Key Word: Corruption Robustness; Domain Adaptation.
  - <details><summary>Digest</summary> We investigate a new scenario called corruption-agnostic robust domain adaptation (CRDA) to equip domain adaptation models with corruption robustness. We take use of information of domain discrepancy to propose a novel module Domain Discrepancy Generator (DDG) for corruption robustness that mimic unpredictable corruptions.

- Gradient Matching for Domain Generalization. [[paper]](https://arxiv.org/abs/2104.09937) [[code]](https://github.com/YugeTen/fish)
  - Yuge Shi, Jeffrey Seely, Philip H.S. Torr, N. Siddharth, Awni Hannun, Nicolas Usunier, Gabriel Synnaeve. *ICLR 2022*
  - Key Word: Domain Generalization, Multi-Source Domain Adaptation.
  - <details><summary>Digest</summary> Here, we propose an inter-domain gradient matching objective that targets domain generalization by maximizing the inner product between gradients from different domains. Since direct optimization of the gradient inner product can be computationally prohibitive -- it requires computation of second-order derivatives -- we derive a simpler first-order algorithm named Fish that approximates its optimization.

- Does enhanced shape bias improve neural network robustness to common corruptions? [[paper]](https://arxiv.org/abs/2104.09789)
  - Chaithanya Kumar Mummadi, Ranjitha Subramaniam, Robin Hutmacher, Julien Vitay, Volker Fischer, Jan Hendrik Metzen. *ICLR 2021*
  - Key Word: Corruption Robustness; Shape-Texture.
  - <details><summary>Digest</summary> While pre-training on stylized images increases both shape bias and corruption robustness, these two quantities are not necessarily correlated: pre-training on edge maps increases the shape bias without consistently helping in terms of corruption robustness.

- Deep Stable Learning for Out-Of-Distribution Generalization. [[paper]](https://arxiv.org/abs/2104.07876) [[code]](https://github.com/xxgege/StableNet)
  - Xingxuan Zhang, Peng Cui, Renzhe Xu, Linjun Zhou, Yue He, Zheyan Shen. *CVPR 2021*
  - Key Word: Stable Learning; Feature Decorrelation.
  - <details><summary>Digest</summary> Conventional methods assume either the known heterogeneity of training data (e.g. domain labels) or the approximately equal capacities of different domains. In this paper, we consider a more challenging case where neither of the above assumptions holds. We propose to address this problem by removing the dependencies between features via learning weights for training samples, which helps deep models get rid of spurious correlations and, in turn, concentrate more on the true connection between discriminative features and labels.

- Misclassification-Aware Gaussian Smoothing improves Robustness against Domain Shifts. [[paper]](https://arxiv.org/abs/2104.01231)
  - Athanasios Tsiligkaridis, Theodoros Tsiligkaridis.
  - Key Word: Corruption Robustness; Regularization.
  - <details><summary>Digest</summary> We introduce a misclassification-aware consistency loss coupled with Gaussian noise regularization and a corresponding training algorithm. Additionally, we present some theoretical properties of our new regularization approach that reveals its connection to local loss geometry.

- Defending Against Image Corruptions Through Adversarial Augmentations. [[paper]](https://arxiv.org/abs/2104.01086)
  - Dan A. Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, Andras Gyorgy, Timothy Mann, Sven Gowal.
  - Key Word: Corruption Robustness; Data Augmentation.
  - <details><summary>Digest</summary> We propose AdversarialAugment, a technique which optimizes the parameters of image-to-image models to generate adversarially corrupted augmented images.

- Improving robustness against common corruptions with frequency biased models. [[paper]](https://arxiv.org/abs/2103.16241)
  - Tonmoy Saikia, Cordelia Schmid, Thomas Brox.
  - Key Word: Corruption Robustness; Frequency.
  - <details><summary>Digest</summary> We propose a new regularization scheme that enforces convolutional feature maps to have a low total variation. We introduce the idea of mixing two experts that specialize in high-frequency and low-frequency robustness.

- Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries. [[paper]](https://arxiv.org/abs/2103.14824)
  - Kun-Peng Ning, Lue Tao, Songcan Chen, Sheng-Jun Huang. *AAAI 2021*