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Machine learning models are often trained on data from one distribution and deployed on others.
Optimization of conditional value-at-risk
R Tyrrell Rockafellar, Stanislav Uryasev, et al · 2000
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Robust dynamic programming
Garud N Iyengar · 2005
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Robust control of markov decision processes with uncertain transition matrices
Arnab Nilim and Laurent El Ghaoui · 2005
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On divergences and informations in statistics and information theory
Friedrich Liese and Igor Vajda · 2006
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Estimation of the warfarin dose with clinical and pharmacogenetic data
International Warfarin Pharmacogenetics Consortium · 2009
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Causality
Judea Pearl · 2009
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Optimal loading dose for the initiation of warfarin: a systematic review
Carl Heneghan, Sally Tyndel, Clare Bankhead, Yi Wan, David Keeling, Rafael Perera, and Alison Ward · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Transportability of causal and statistical relations: A formal approach
Judea Pearl and Elias Bareinboim · 2011
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Robust solutions of optimization problems affected by uncertain probabilities
Aharon Ben-Tal, Dick Den Hertog, Anja De Waegenaere, Bertrand Melenberg, and Gijs Rennen · 2013
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Lectures on stochastic programming: modeling and theory
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Scaling up robust mdps using function approximation
Aviv Tamar, Shie Mannor, and Huan Xu · 2014
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Online decision-making with high-dimensional covariates
Hamsa Bastani and Mohsen Bayati · 2015
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Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
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Data-efficient off-policy policy evaluation for reinforcement learning
Philip Thomas and Emma Brunskill · 2016
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Distributionally robust deep learning as a generalization of adversarial training
Matthew Staib and Stefanie Jegelka · 2017
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Transfer learning in multi-armed bandit: a causal approach
Junzhe Zhang and Elias Bareinboim · 2017
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Data-driven robust optimization
Dimitris Bertsimas, Vishal Gupta, and Nathan Kallus · 2018
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Woulda, coulda, shoulda: Counterfactually-guided policy search
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Learning models with uniform performance via distributionally robust optimization
John Duchi and Hongseok Namkoong · 2018
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Does distributionally robust supervised learning give robust classifiers?
Weihua Hu, Gang Niu, Issei Sato, and Masashi Sugiyama · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Domain adaptation by using causal inference to predict invariant conditional distributions
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, and Joris M Mooij · 2018
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Distributionally robust counterfactual risk minimization
Louis Faury, Ugo Tanielian, Elvis Dohmatob, Elena Smirnova, and Flavian Vasile · 2020
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Popcorn: Partially observed prediction constrained reinforcement learning
Joseph Futoma, Michael C Hughes, and Finale Doshi-Velez · 2020
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Causal Inference: What If
Robins JM Hernán MA · 2020
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Robust causal inference under covariate shift via worst-case subpopulation treatment effects
Sookyo Jeong and Hongseok Namkoong · 2020
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Off-policy evaluation and learning for external validity under a covariate shift
Masahiro Kato, Masatoshi Uehara, and Shota Yasui · 2020
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Invariant models for causal transfer learning
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Anchor regression: heterogeneous data meets causality
Dominik Rothenhäusler, Nicolai Meinshausen, Peter Bühlmann, and Jonas Peters · 2018
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Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John Duchi · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Quantifying distributional model risk via optimal transport
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Distributionally robust losses against mixture covariate shifts
John C Duchi, Tatsunori Hashimoto, and Hongseok Namkoong · 2019
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Taylor W Killian, Marzyeh Ghassemi, and Shalmali Joshi · 2020
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Out-of-distribution generalization with maximal invariant predictor
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Out-of-distribution generalization via risk extrapolation (rex)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Remi Le Priol, and Aaron Courville · 2020
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Luchen Li, Ignacio Albert-Smet, and Aldo A Faisal · 2020
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Learning optimal distributionally robust individualized treatment rules
Weibin Mo, Zhengling Qi, and Yufeng Liu · 2020
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Off-policy policy evaluation for sequential decisions under unobserved confounding
Hongseok Namkoong, Ramtin Keramati, Steve Yadlowsky, and Emma Brunskill · 2020
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Understanding and mitigating the tradeoff between robustness and accuracy
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, and Percy Liang · 2020
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The risks of invariant risk minimization
Elan Rosenfeld, Pradeep Ravikumar, and Andrej Risteski · 2020
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Distributional robust batch contextual bandits
Nian Si, Fan Zhang, Zhengyuan Zhou, and Jose Blanchet · 2020
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Evaluating model robustness to dataset shift
Adarsh Subbaswamy, Roy Adams, and Suchi Saria · 2020
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Measuring robustness to natural distribution shifts in image classification
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors · 2020
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Risk variance penalization: From distributional robustness to causality
Chuanlong Xie, Fei Chen, Yue Liu, and Zhenguo Li · 2020
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