Survey for Distribution Shift

Domain Adaptation

• Cai, Ruichu, et al. "Time Series Domain Adaptation via Sparse Associative Structure Alignment." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. 2020.
• Liu, Xiaofeng, et al. "Subtype-aware Unsupervised Domain Adaptation for Medical Diagnosis." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. 2020.
• Tachet des Combes, Remi, et al. "Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Chen, Yining, et al. "Self-training Avoids Using Spurious Features Under Domain Shift." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Kang, Guoliang, et al. "Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Venkat, Naveen, et al. "Your Classifier can Secretly Suffice Multi-Source Domain Adaptation." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Zhang, Kun, et al. "Domain adaptation as a problem of inference on graphical models." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020. [12] Cui, Shuhao, et al. "Heuristic Domain Adaptation." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Park, Kwanyong, et al. "Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Ge, Yixiao, et al. "Self-paced contrastive learning with hybrid memory for domain adaptive object re-id." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Balaji, Yogesh, Rama Chellappa, and Soheil Feizi. "Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Saito, Kuniaki, et al. "Universal domain adaptation through self supervision." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Wang, Ximei, et al. "Transferable Calibration with Lower Bias and Variance in Domain Adaptation." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Combes, Remi Tachet des, et al. "Domain adaptation with conditional distribution matching and generalized label shift." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Luo, Yawei, et al. "Adversarial style mining for one-shot unsupervised domain adaptation." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Zhang, Chao, Lei Zhang, and Jieping Ye. 2012. “Generalization Bounds for Domain Adaptation.” Advances in Neural Information Processing Systems 4: 3320–28.
• Ben-David, S., J. Blitzer, and K. Crammer. 2007. “Analysis of Representations for Domain Adaptation.” Advances in Neural Information Processing Systems. http://papers.nips.cc/paper/2983-analysis-of-representations-for-domain-adaptation.pdf.

Covariate Shift

• Reisizadeh, Amirhossein, et al. "Robust Federated Learning: The Case of Affine Distribution Shifts." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Fang, Tongtong, et al. "Rethinking Importance Weighting for Deep Learning under Distribution Shift." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Schneider, Steffen, et al. "Improving robustness against common corruptions by covariate shift adaptation." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020
• Zhang, Tianyi, Ikko Yamane, Nan Lu, and Masashi Sugiyama. 2020. “A One-Step Approach to Covariate Shift Adaptation.” arXiv [cs.LG]. arXiv. http://proceedings.mlr.press/v129/zhang20a/zhang20a.pdf.
• Tibshirani, Ryan J., Rina Foygel Barber, Emmanuel J. Candes, and Aaditya Ramdas. 2019. “Conformal Prediction Under Covariate Shift.” arXiv [stat.ME]. arXiv. http://arxiv.org/abs/1904.06019.
• Yamada, Makoto, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, and Masashi Sugiyama. 2011. “Relative Density-Ratio Estimation for Robust Distribution Comparison.” In Advances in Neural Information Processing Systems, edited by J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Q. Weinberger, 24:594–602. Curran Associates, Inc.
• Cortes, Corinna, Mehryar Mohri, Michael Riley, and Afshin Rostamizadeh. 2008. “Sample Selection Bias Correction Theory.” In Algorithmic Learning Theory, 38–53. Springer Berlin Heidelberg.
• Sugiyama, Masashi, Shinichi Nakajima, Hisashi Kashima, Paul Von Buenau, and Motoaki Kawanabe. 2007. “Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation.” In NIPS, 7:1433–40. Citeseer.
• Shimodaira, Hidetoshi. 2000. “Improving Predictive Inference under Covariate Shift by Weighting the Log-Likelihood Function.” Journal of Statistical Planning and Inference 90 (2): 227–44.

Target Shift

• Redko, Ievgen, Nicolas Courty, Rémi Flamary, and Devis Tuia. 2019. “Optimal Transport for Multi-Source Domain Adaptation under Target Shift.” In Proceedings of Machine Learning Research, edited by Kamalika Chaudhuri and Masashi Sugiyama, 89:849–58. Proceedings of Machine Learning Research. PMLR.
• Azizzadenesheli, Kamyar, Anqi Liu, Fanny Yang, and Animashree Anandkumar. 2019. “Regularized Learning for Domain Adaptation under Label Shifts.” arXiv [cs.LG]. arXiv. http://arxiv.org/abs/1903.09734.
• Lipton, Zachary, Yu-Xiang Wang, and Alexander Smola. 2018. “Detecting and Correcting for Label Shift with Black Box Predictors.” In Proceedings of the 35th International Conference on Machine Learning, edited by Jennifer Dy and Andreas Krause, 80:3122–30. Proceedings of Machine Learning Research. Stockholmsmässan, Stockholm Sweden: PMLR.

Distribution Shift Detection

• Kulinski, Sean, Saurabh Bagchi, and David I. Inouye. "Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.

Out-Of-Distribution Detection

• Tack, Jihoon, et al. "CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Teney, Damien, et al. "On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Jeong, Taewon, and Heeyoung Kim. "OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Nandy, Jay, Wynne Hsu, and Mong Li Lee. "Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Bitterwolf, Julian, Alexander Meinke, and Matthias Hein. "Certifiably Adversarially Robust Detection of Out-of-Distribution Data." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Kirichenko, Polina, Pavel Izmailov, and Andrew Gordon Wilson. "Why normalizing flows fail to detect out-of-distribution data." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Xiao, Zhisheng, Qing Yan, and Yali Amit. "Likelihood regret: An out-of-distribution detection score for variational auto-encoder." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Liu, Weitang, et al. "Energy-based Out-of-distribution Detection." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.

Sampling Bias, Selection Bias

• Purushwalkam, Senthil, and Abhinav Gupta. 2020. “Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases.” 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Mazaheri, Bijan, Siddharth Jain, and Jehoshua Bruck. 2020. “Robust Correction of Sampling Bias Using Cumulative Distribution Functions.” 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Flanigan, Bailey, Paul Gölz, Anupam Gupta, and Ariel Procaccia. 2020. “Neutralizing Self-Selection Bias in Sampling for Sortition.” 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.

Meta-Analysis

• Taori, Rohan, et al. "Measuring robustness to natural distribution shifts in image classification." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.

Distributionally Robust Optimization (DRO)

• Duchi, John C., Peter W. Glynn, and Hongseok Namkoong. "Statistics of robust optimization: A generalized empirical likelihood approach." Mathematics of Operations Research (2021).
• Subbaswamy, Adarsh, Roy Adams, and Suchi Saria. 2021. “Evaluating Model Robustness to Dataset Shift.” In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR. http://arxiv.org/abs/2010.15100.
• Levy, Daniel, et al. "Large-Scale Methods for Distributionally Robust Optimization." 34th Conference on Neural Information Processing Systems (NeurIPS) 2020. Neural Information Processing Systems, 2020.
• Lei, Jing. "Convergence and concentration of empirical measures under Wasserstein distance in unbounded functional spaces." Bernoulli 26.1 (2020): 767-798.
• Duchi, John, and Hongseok Namkoong. "Variance-based regularization with convex objectives." The Journal of Machine Learning Research 20.1 (2019): 2450-2504.
• Bertsimas, Dimitris, Vishal Gupta, and Nathan Kallus. "Data-driven robust optimization." Mathematical Programming 167.2 (2018): 235-292.
• Esfahani, Peyman Mohajerin, and Daniel Kuhn. "Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations." Mathematical Programming 171.1 (2018): 115-166.
• Gao, Rui, and Anton J. Kleywegt. "Distributionally robust stochastic optimization with Wasserstein distance." arXiv preprint arXiv:1604.02199 (2016).
• Namkoong, Hongseok, and John C. Duchi. "Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences." NIPS. Vol. 29. 2016.
• Ben-Tal, Aharon, et al. "Robust solutions of optimization problems affected by uncertain probabilities." Management Science 59.2 (2013): 341-357.

Survey

• Gabrel, Virginie, Cécile Murat, and Aurélie Thiele. "Recent advances in robust optimization: An overview." European journal of operational research 235.3 (2014): 471-483.

Stability Analysis

• Subbaswamy, Adarsh, Roy Adams, and Suchi Saria. "Evaluating Model Robustness and Stability to Dataset Shift." International Conference on Artificial Intelligence and Statistics. PMLR, 2021.