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The key of the out-of-distribution (OOD) generalization is to generalize invariance from training domains to target domains.
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Domain adaptation under target and conditional shift
Zhang, K., Schölkopf, B., Muandet, K., and Wang, Z · 2013
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A simple method to determine if a music information retrieval system is a “horse”
Sturm, B. L · 2014
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Distributionally robust logistic regression
Shafieezadeh Abadeh, S., Mohajerin Esfahani, P. M., and Kuhn, D · 2015
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Multi-source domain adaptation: A causal view
Zhang, K., Gong, M., and Schölkopf, B · 2015
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Statistics of robust optimization: A generalized empirical likelihood approach
Duchi, J., Glynn, P., and Namkoong, H · 2016
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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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Revisiting visual question answering baselines
Jabri, A., Joulin, A., and Van Der Maaten, L · 2016
Domain adaptation by using causal inference to predict invariant conditional distributions
Magliacane, S., van Ommen, T., Claassen, T., Bongers, S., Versteeg, P., and Mooij, J. M · 2018
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Causality from a distributional robustness point of view
Meinshausen, N · 2018
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Invariant models for causal transfer learning
Rojas-Carulla, M., Schölkopf, B., Turner, R., and Peters, J · 2018
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Anchor regression: heterogeneous data meets causality
Rothenhäusler, D., Meinshausen, N., Bühlmann, P., and Peters, J · 2018
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Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
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Data-driven chance constrained stochastic program
Jiang, R. and Guan, Y · 2016
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Causal inference by using invariant prediction: identification and confidence intervals
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The empirical likelihood approach to quantifying uncertainty in sample average approximation
Lam, H. and Zhou, E · 2017
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Deeper, broader and artier domain generalization
Li, D., Yang, Y., Song, Y.-Z., and Hospedales, T. M · 2017
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Variance-based regularization with convex objectives
Namkoong, H. and Duchi, J. C · 2017
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Variance-based regularization with convex objectives
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Distributionally robust language modeling
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Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2019
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High-dimensional probability, 2019
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In search of lost domain generalization
Gulrajani, I. and Lopez-Paz, D · 2020
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Causal discovery from heterogeneous/nonstationary data
Huang, B., Zhang, K., Zhang, J., Ramsey, J., Sanchez-Romero, R., Glymour, C., and Schölkopf, B · 2020
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Out-of-distribution generalization with maximal invariant predictor
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The risks of invariant risk minimization
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Out-of-distribution generalization analysis via influence function
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Does distributionally robust supervised learning give robust classifiers?
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