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As opposed to standard empirical risk minimization (ERM), distributionally robust optimization aims to minimize the worst-case risk over a larger ambiguity set containing the original empirical distribution of the training data.
Statistical Learning Theory
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Rademacher and gaussian complexities: Risk bounds and structural results
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Empirical margin distributions and bounding the generalization error of combined classifiers
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Statistics of robust optimization: a generalized empirical likelihood approach
J. C. Duchi, P. W. Glynn, and H. Namkoong · 2016
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Distributionally robust stochastic optimization with Wasserstein distance
R. Gao and A. J. Kleywegt · 2016
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Optimal transport for domain adaptation
N. Courty, R. Flamary, D. Tuia, and A. Rakotomamonjy · 2017
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Data-driven distributionally robust optimization using the Wasserstein metric: performance guarantees and tractable reformulations
Peyman Mohajerin Esfahani and Daniel Kuhn · 2018
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Distributionally robust logistic regression
S. Shafieezadeh-Abadeh, P. Mohajerin Esfahani, and D. Kuhn · 2015
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Certifying some distributional robustness with principled adversarial training
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