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Training machine learning models robust to distribution shifts is critical for real-world applications.
Convex analysis , volume 18
R Tyrrell Rockafellar · 1970
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Covering numbers for real-valued function classes
Peter L Bartlett, Sanjeev R Kulkarni, and S Eli Posner · 1997
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Some pac-bayesian theorems
David A McAllester · 1998
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Convex optimization
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe · 2004
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Robust supervised learning
J Andrew Bagnell · 2005
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Pac-bayesian supervised classification: the thermodynamics of statistical learning
Olivier Catoni · 2007
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The minimum description length principle
Peter D Grünwald · 2007
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Robust solutions of optimization problems affected by uncertain probabilities
Aharon Ben-Tal, Dick Den Hertog, Anja De Waegenaere, Bertrand Melenberg, and Gijs Rennen · 2013
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Divide and conquer kernel ridge regression
Yuchen Zhang, John Duchi, and Martin Wainwright · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Robust classification under sample selection bias
Anqi Liu and Brian Ziebart · 2014
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Robust learning under uncertain test distributions: Relating covariate shift to model misspecification
Junfeng Wen, Chun-Nam Yu, and Russell Greiner · 2014
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Distributionally robust logistic regression
Soroosh Shafieezadeh Abadeh, Peyman M Mohajerin Esfahani, and Daniel Kuhn · 2015
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Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
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Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2016
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Demographic dialectal variation in social media: A case study of african-american english
Su Lin Blodgett, Lisa Green, and Brendan O’Connor · 2016
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Statistics of robust optimization: A generalized empirical likelihood approach
John Duchi, Peter Glynn, and Hongseok Namkoong · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Stochastic gradient methods for distributionally robust optimization with f-divergences
Hongseok Namkoong and John C Duchi · 2016
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Faster rates for convex-concave games
Jacob Abernethy, Kevin A Lai, Kfir Y Levy, and Jun-Kun Wang · 2018
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Data-driven robust optimization
Dimitris Bertsimas, Vishal Gupta, and Nathan Kallus · 2018
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Fairness without demographics in repeated loss minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
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Does distributionally robust supervised learning give robust classifiers?
Weihua Hu, Gang Niu, Issei Sato, and Masashi Sugiyama · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu · 2018
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2020
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Large-scale methods for distributionally robust optimization
Daniel Levy, Yair Carmon, John C Duchi, and Aaron Sidford · 2020
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Learning from failure: De-biasing classifier from biased classifier
Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, and Jinwoo Shin · 2020
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An investigation of why overparameterization exacerbates spurious correlations
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, and Percy Liang · 2020
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The pitfalls of simplicity bias in neural networks
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, and Praneeth Netrapalli · 2020
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Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy Hospedales · 2018
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Detecting and correcting for label shift with black box predictors
Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii · 2018
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The implicit bias of gradient descent on separable data
Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro · 2018
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An empirical study of example forgetting during deep neural network learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Quantifying distributional model risk via optimal transport
Jose Blanchet and Karthyek Murthy · 2019
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Nimit Sohoni, Jared Dunnmon, Geoffrey Angus, Albert Gu, and Christopher Ré · 2020
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Environment inference for invariant learning
Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel · 2021
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Learning models with uniform performance via distributionally robust optimization
John C. Duchi and Hongseok Namkoong · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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Just train twice: Improving group robustness without training group information
Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, and Chelsea Finn · 2021
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Greedy adversarial equilibrium: an efficient alternative to nonconvex-nonconcave min-max optimization
Oren Mangoubi and Nisheeth K Vishnoi · 2021
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Is importance weighting incompatible with interpolating classifiers?
Ke Alexander Wang, Niladri S Chatterji, Saminul Haque, and Tatsunori Hashimoto · 2021
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Boosted cvar classification
Runtian Zhai, Chen Dan, Arun Suggala, J Zico Kolter, and Pradeep Ravikumar · 2021
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Learning to split for automatic bias detection
Yujia Bao and Regina Barzilay · 2022
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Simple data balancing achieves competitive worst-group-accuracy
Badr Youbi Idrissi, Martin Arjovsky, Mohammad Pezeshki, and David Lopez-Paz · 2022
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Last layer re-training is sufficient for robustness to spurious correlations
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2022
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Diversify and disambiguate: Learning from underspecified data
Yoonho Lee, Huaxiu Yao, and Chelsea Finn · 2022
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Importance tempering: Group robustness for overparameterized models
Yiping Lu, Wenlong Ji, Zachary Izzo, and Lexing Ying · 2022
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Elan Rosenfeld, Pradeep Ravikumar, and Andrej Risteski · 2022
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Unsupervised learning of debiased representations with pseudo-attributes
Seonguk Seo, Joon-Young Lee, and Bohyung Han · 2022
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Learning from noisy labels with deep neural networks: A survey
Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee · 2022
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Improving out-of-distribution robustness via selective augmentation
Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, and Chelsea Finn · 2022
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