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Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e.g., generalization across distributions) is valued.
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Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, and Andrew Gordon Wilson · 2018
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Han Zhao, Remi Tachet des Combes, Kun Zhang, and Geoffrey J. Gordon · 2019
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Alexandra Chouldechova and Aaron Roth · 2020
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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
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Fabio M Carlucci, Paolo Russo, Tatiana Tommasi, and Barbara Caputo · 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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Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
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Not all samples are created equal: Deep learning with importance sampling
Angelos Katharopoulos and François Fleuret · 2018
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Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2018
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Domain generalization with adversarial feature learning
Haoliang Li, Sinno Jialin Pan, Shiqi Wang, and Alex C Kot · 2018
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The origins and prevalence of texture bias in convolutional neural networks
Katherine Hermann, Ting Chen, and Simon Kornblith · 2020
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Self-challenging improves cross-domain generalization
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Myeongjin Kim and Hyeran Byun · 2020
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Out-of-distribution generalization with maximal invariant predictor
Masanori Koyama and Shoichiro Yamaguchi · 2020
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Fairness without demographics through adversarially reweighted learning
Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed Chi · 2020
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Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, and Jinwoo Shin · 2020
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Giambattista Parascandolo, Alexander Neitz, Antonio Orvieto, Luigi Gresele, and Bernhard Schölkopf · 2020
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High-frequency component helps explain the generalization of convolutional neural networks
Haohan Wang, Xindi Wu, Zeyi Huang, and Eric P Xing · 2020
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Z Wang and X Cheng · 2020
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Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, and Chelsea Finn · 2020
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Domain generalization via entropy regularization
Shanshan Zhao, Mingming Gong, Tongliang Liu, Huan Fu, and Dacheng Tao · 2020
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Learning domain invariant representations in goal-conditioned block mdps
Beining Han, Chongyi Zheng, Harris Chan, Keiran Paster, Michael R Zhang, and Jimmy Ba · 2021
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Towards non-iid image classification: A dataset and baselines
Yue He, Zheyan Shen, and Peng Cui · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, et al · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, et al · 2021
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Out-of-distribution generalization via risk extrapolation (rex)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville · 2021
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A survey on bias and fairness in machine learning
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan · 2021
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Domain invariant representation learning with domain density transformations
A Tuan Nguyen, Toan Tran, Yarin Gal, and Atılım Güneş Baydin · 2021
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Discriminative domain-invariant adversarial network for deep domain generalization
Mohammad Mahfujur Rahman, Clinton Fookes, and Sridha Sridharan · 2021
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Toward learning human-aligned cross-domain robust models by countering misaligned features
Haohan Wang, Zeyi Huang, Hanlin Zhang, and Eric Xing · 2021
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Ood-bench: Benchmarking and understanding out-of-distribution generalization datasets and algorithms
Nanyang Ye, Kaican Li, Lanqing Hong, Haoyue Bai, Yiting Chen, Fengwei Zhou, and Zhenguo Li · 2021
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