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Machine learning algorithms with empirical risk minimization usually suffer from poor generalization performance due to the greedy exploitation of correlations among the training data, which are not stable under distributional shifts.
Transductive reliability estimation for medical diagnosis
Kukar, M · 2003
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Out-of-distribution generalization with maximal invariant predictor
Koyama, M. and Yamaguchi, S · 2008
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Network information theory
El Gamal, A. and Kim, Y.-H · 2011
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Invariant models for causal transfer learning
Rojas-Carulla, M., Schlkopf, B., Turner, R., and Peters, J · 2015
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Domain adaptation with conditional transferable components
Gong, M., Zhang, K., Liu, T., Tao, D., Glymour, C., and Schölkopf, B · 2016
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UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
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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
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Learning models with uniform performance via distributionally robust optimization
Duchi, J. and Namkoong, H · 2018
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Data-driven distributionally robust optimization using the wasserstein metric: performance guarantees and tractable reformulations
Esfahani, P. M. and Kuhn, D · 2018
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Does distributionally robust supervised learning give robust classifiers?
Hu, W., Niu, G., Sato, I., and Sugiyama, M · 2018
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Optimized scoring systems: Toward trust in machine learning for healthcare and criminal justice
Rudin, C. and Ustun, B · 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
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Certifying some distributional robustness with principled adversarial training
Sinha, A., Namkoong, H., and Duchi, J · 2018
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Task2vec: Task embedding for meta-learning
Incorporating unlabeled data into distributionally robust learning
Frogner, C., Claici, S., Chien, E., and Solomon, J · 2019
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Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2019
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Invariant rationalization
Chang, S., Zhang, Y., Yu, M., and Jaakkola, T. S · 2020
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Environment inference for invariant learning
Creager, E., Jacobsen, J.-H., and Zemel, R · 2020
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Stable prediction with model misspecification and agnostic distribution shift
Kuang, K., Xiong, R., Cui, P., Athey, S., and Li, B · 2020
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Achille, A., Lam, M., Tewari, R., Ravichandran, A., Maji, S., Fowlkes, C. C., Soatto, S., and Perona, P · 2019
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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An investigation of why overparameterization exacerbates spurious correlations
Sagawa, S., Raghunathan, A., Koh, P. W., and Liang, P · 2020
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Feature selection using stochastic gates
Yamada, Y., Lindenbaum, O., Negahban, S., and Kluger, Y · 2020
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