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Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts due to the greedy adoption of all the correlations found in training data.
Big but Imperceptible Adversarial Perturbations via Semantic Manipulation
Bhattad, A.; Chong, M. J.; Liang, K.; Li, B.; and Forsyth, D. A. 2019 · 1904
Earlier work this paper cites.
Arjovsky, M.; Bottou, L.; Gulrajani, I.; and Lopez-Paz, D. 2019 · 1907
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Sagawa, S.; Koh, P. W.; Hashimoto, T. B.; and Liang, P. 2019 · 1911
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Can Attention Masks Improve Adversarial Robustness?
Vaishnavi, P.; Cong, T.; Eykholt, K.; Prakash, A.; and Rahmati, A. 2019 · 1911
Earlier work this paper cites.
Incorporating Unlabeled Data into Distributionally Robust Learning
Frogner, C.; Claici, S.; Chien, E.; and Solomon, J. 2019 · 1912
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Robust convex optimization
Ben-Tal, A.; and Nemirovski, A. 1998 · 1998
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Rademacher and Gaussian complexities: Risk bounds and structural results
Bartlett, P. L.; and Mendelson, S. 2002 · 2002
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Transductive reliability estimation for medical diagnosis
Kukar, M. 2003 · 2003
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Domain adaptation for statistical classifiers
Daume, H.; and Marcu, D. 2006 · 2006
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Unbiased look at dataset bias 1521–1528
Torralba, A.; and Efros, A. A. 2011 · 2011
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The caltech-ucsd birds-200-2011 dataset
Wah, C.; Branson, S.; Welinder, P.; Perona, P.; and Belongie, S. 2011 · 2011
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Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
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An empirical evaluation of deep learning on highway driving
Huval, B.; Wang, T.; Tandon, S.; Kiske, J.; Song, W.; Pazhayampallil, J.; Andriluka, M.; Rajpurkar, P.; Migimatsu, T.; Cheng-Yue, R.; et al. 2015 · 2015
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The limitations of deep learning in adversarial settings
Papernot, N.; McDaniel, P.; Jha, S.; Fredrikson, M.; Celik, Z. B.; and Swami, A. 2016 · 2016
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UCI Machine Learning Repository
Dua, D.; and Graff, C. 2017 · 2017
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Stable prediction across unknown environments
Kuang, K.; Cui, P.; Athey, S.; Xiong, R.; and Li, B. 2018 · 2018
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Optimized scoring systems: Toward trust in machine learning for healthcare and criminal justice
Rudin, C.; and Ustun, B. 2018 · 2018
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Causally Regularized Learning with Agnostic Data Selection Bias
Shen, Z.; Cui, P.; Kuang, K.; Li, B.; and Chen, P. 2018 · 2018
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Certifying Some Distributional Robustness with Principled Adversarial Training
Sinha, A.; Namkoong, H.; and Duchi, J. 2018 · 2018
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Bayesian adversarial learning
Ye, N.; and Zhu, Z. 2018 · 2018
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Stable Learning via Sample Reweighting
Shen, Z.; Cui, P.; Zhang, T.; and Kuang, K. 2019 · 2019
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Fairness in criminal justice risk assessments: The state of the art
Berk, R.; Heidari, H.; Jabbari, S.; Kearns, M.; and Roth, A. 2018 · 2018
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Learning models with uniform performance via distributionally robust optimization
Duchi, J.; and Namkoong, H. 2018 · 2018
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