Dec 17, 2017 · In this paper, we develop a general theory on the variation regularization effect of the Wasserstein DRO - a new form of regularization that generalizes total- ...
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Nov 1, 2022 · Wasserstein distributionally robust optimization (DRO) is an approach to optimization under uncertainty in which the decision maker hedges against a set of ...
We establish a connection between such Wasserstein DRSO and regularization. Specifically, we identify a broad class of loss functions, for which the Wasserstein ...
Dec 17, 2017 · Such relation provides new interpretations for problems involving regularization, including a great number of statistical learning problems and ...
Abstract. We propose an adjusted Wasserstein distributionally robust estimator—based on a non- linear transformation of the Wasserstein distributionally ...
Wasserstein distributionally robust optimization (DRO) has recently achieved empirical success for various applica- tions in operations research and machine ...
Our study aims to facilitate robust reinforcement learning by establishing a dual relation between robust MDPs and regularization. We introduce Wasserstein ...
In recent years, Wasserstein Distributionally Robust Optimization (DRO) has garnered substantial interest for its efficacy in data-driven decision-making ...
Mar 23, 2023 · Distributionally robust optimization (DRO) has recently been formulated using OT metrics and has proven to be useful in machine learning (see ...
Feb 12, 2025 · Robust wasserstein profile inference and applications to machine learning. Journal of Applied Probability, 56(3):830–857. (4) Blanchet, J ...
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