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We present a general duality result for Wasserstein distributionally robust optimization that holds for any Kantorovich transport cost, measurable loss function, and nominal probability distribution.
Rockafellar RT (1970) Convex analysis . Number 28 in Princeton Mathematical Series (Princeton university press)
1970
Earlier work this paper cites.
Castaing C, Valadier M (1977) Measurable multifunctions. Convex Analysis and Measurable Multifunctions , 59–90 (Springer)
1977
Earlier work this paper cites.
Shreve SE, Bertsekas DP (1979) Universally measurable policies in dynamic programming. Mathematics of Operations Research 4(1):15–30
1979
Earlier work this paper cites.
Bertsekas DP, Shreve SE (1996) Stochastic optimal control: the discrete-time case , volume 5 (Athena Scientific)
1996
Earlier work this paper cites.
Kallenberg O (1997) Foundations of modern probability , volume 2 (Springer)
1997
Earlier work this paper cites.
Luenberger DG (1997) Optimization by vector space methods (John Wiley & Sons)
1997
Earlier work this paper cites.
Ambrosio L, Fusco N, Pallara D (2000) Functions of bounded variation and free discontinuity problems (Oxford University Press)
2000
Earlier work this paper cites.
Rockafellar RT, Uryasev S, et al. (2000) Optimization of conditional value-at-risk. Journal of risk 2:21–42
2000
Earlier work this paper cites.
Shapiro A (2001) On duality theory of conic linear problems. Semi-infinite programming , 135–165 (Springer)
2001
Earlier work this paper cites.
Aliprantis CD, Border K (2006) Infinite Dimensional Analysis: A Hitchhiker’s Guide (Springer Science & Business Media)
2006
Earlier work this paper cites.
Aubin JP, Frankowska H (2009) Set-valued analysis (Springer Science & Business Media)
2009
Cited alongside, same era.
Rockafellar RT, Wets RJB (2009) Variational analysis , volume 317 (Springer Science & Business Media)
2009
Cited alongside, same era.
Föllmer H, Schied A (2010) Convex and coherent risk measures. Encyclopedia of Quantitative Finance 355–363
2010
Cited alongside, same era.
Shapiro A (2017) Interchangeability principle and dynamic equations in risk averse stochastic programming. Operations Research Letters 45(4):377–381
2017
Cited alongside, same era.
Yang I (2017) A convex optimization approach to distributionally robust markov decision processes with Wasserstein distance. IEEE control systems letters 1(1):164–169
2017
Cited alongside, same era.
Blanchet J, Murthy K, Nguyen VA (2021) Statistical analysis of Wasserstein distributionally robust estimators. Tutorials in Operations Research: Emerging Optimization Methods and Modeling Techniques with Applications , 227–254 (INFORMS)
2021
Later among the works it cites.
Shapiro A, Dentcheva D, Ruszczynski A (2021) Lectures on stochastic programming: modeling and theory (SIAM)
2021
Later among the works it cites.
Xie W (2021) On distributionally robust chance constrained programs with Wasserstein distance. Mathematical Programming 186(1):115–155
2021
Later among the works it cites.
Wang J, Gao R, Zha H (2022) Reliable off-policy evaluation for reinforcement learning. Operations Research Forthcoming
2022
Closest in time.
Yang Z, Gao R (2022) Wasserstein Regularization for 0-1 Loss. Optimization Online preprint
2022
Closest in time.
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2018
Cited alongside, same era.
Sinha A, Namkoong H, Duchi J (2018) Certifying some distributional robustness with principled adversarial training. International Conference on Learning Representations
2018
Cited alongside, same era.
Zhao C, Guan Y (2018) Data-driven risk-averse stochastic optimization with Wasserstein metric. Operations Research Letters 46(2):262–267
2018
Cited alongside, same era.
Blanchet J, Murthy K (2019) Quantifying distributional model risk via optimal transport. Mathematics of Operations Research 44(2):565–600
2019
Cited alongside, same era.
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2019
Cited alongside, same era.
Chen Z, Kuhn D, Wiesemann W (2023) On approximations of data-driven chance constrained programs over Wasserstein balls. Operations Research Letters 51(3):226–233
2023
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2023
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Liu F, Chen Z, Wang S (2023) Globalized distributionally robust counterpart. INFORMS Journal on Computing 35(5):1120–1142
2023
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