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Distributional robustness is a central goal of prediction algorithms due to the prevalent distribution shifts in real-world data.
Stabilizing variable selection and regression
N. Pfister, E. G. Williams, J. Peters, R. Aebersold, and P. Bühlmann · 1911
Earlier work this paper cites.
Symmetric Multivariate and Related Distributions
K.-T. Fang · 1990
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A semidefinite programming approach to optimal-moment bounds for convex classes of distributions
I. Popescu · 2005
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Domain adaptation: Learning bounds and algorithms
Y. Mansour, M. Mohri, and A. Rostamizadeh · 2009
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Distributionally robust optimization under moment uncertainty with application to data-driven problems
E. Delage and Y. Ye · 2010
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Auto-encoding variational bayes, 2013
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Distributionally robust convex optimization
W. Wiesemann, D. Kuhn, and M. Sim · 2013
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I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Adam: A method for stochastic optimization
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Causal inference by using invariant prediction: identification and confidence intervals
J. Peters, P. Bühlmann, and N. Meinshausen · 2016
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Conic programming reformulations of two-stage distributionally robust linear programs over wasserstein balls
G. A. Hanasusanto and D. Kuhn · 2017
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Causal effect inference with deep latent-variable models
C. Louizos, U. Shalit, J. M. Mooij, D. Sontag, R. Zemel, and M. Welling · 2017
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
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Data-driven robust optimization
D. Bertsimas, V. Gupta, and N. Kallus · 2018
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Data-driven distributionally robust optimization using the wasserstein metric: Performance guarantees and tractable reformulations
P. Mohajerin Esfahani and D. Kuhn · 2018
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Invariant models for causal transfer learning
M. Rojas-Carulla, B. Schölkopf, R. Turner, and J. Peters · 2018
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M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz · 2019
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Wasserstein distributionally robust optimization: Theory and applications in machine learning
D. Kuhn, P. M. Esfahani, V. A. Nguyen, and S. Shafieezadeh-Abadeh · 2019
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Distributionally robust reinforcement learning
E. Smirnova, E. Dohmatob, and J. Mary · 2019
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Mapping information-rich genotype-phenotype landscapes with genome-scale perturb-seq
J. M. Replogle, R. A. Saunders, A. N. Pogson, J. A. Hussmann, A. Lenail, A. Guna, L. Mascibroda, E. J. Wagner, K. Adelman, G. Lithwick-Yanai, N. Iremadze, F. Oberstrass, D. Lipson, J. L. Bonnar, M. Jost, T. M. Norman, and J. S. Weissman · 2022
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An online learning approach to interpolation and extrapolation in domain generalization
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Nonlinear ica using volume-preserving transformations
X. Yang, Y. Wang, J. Sun, X. Zhang, S. Zhang, Z. Li, and J. Yan · 2022
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Interventional causal representation learning
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Identifiable deep generative models via sparse decoding
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Toward causal representation learning
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Causality-oriented robustness: exploiting general additive interventions
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Posterior collapse and latent variable non-identifiability
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Wasserstein distributionally robust optimization and variation regularization
R. Gao, X. Chen, and A. J. Kleywegt · 2024
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M. Lu, H. Zhong, T. Zhang, and J. Blanchet · 2024
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Distributional principal autoencoders
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A unified theory of robust and distributionally robust optimization via the primal-worst-equals-dual-best principle
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