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Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning

Daniel Kuhn(daniel.kuhn***at***epfl.ch)
Peyman Mohajerin Esfahani(P.MohajerinEsfahani***at***tudelft.nl)
Viet Anh Nguyen(viet-anh.nguyen***at***epfl.ch)
Soroosh Shafieezadeh-Abadeh(soroosh.shafiee***at***epfl.ch)

Abstract: Many decision problems in science, engineering and economics are affected by uncertain parameters whose distribution is only indirectly observable through samples. The goal of data-driven decision-making is to learn a decision from finitely many training samples that will perform well on unseen test samples. This learning task is difficult even if all training and test samples are drawn from the same distribution---especially if the dimension of the uncertainty is large relative to the training sample size. Wasserstein distributionally robust optimization seeks data-driven decisions that perform well under the most adverse distribution within a certain Wasserstein distance from a nominal distribution constructed from the training samples. In this tutorial we will argue that this approach has many conceptual and computational benefits. Most prominently, the optimal decisions can often be computed by solving tractable convex optimization problems, and they enjoy rigorous out-of-sample and asymptotic consistency guarantees. We will also show that Wasserstein distributionally robust optimization has interesting ramifications for statistical learning and motivates new approaches for fundamental learning tasks such as classification, regression, maximum likelihood estimation or minimum mean square error estimation, among others.

Keywords: distributionally robust optimization; data-driven optimization; Wasserstein distance; optimizer's curse; machine learning; regularization

Category 1: Robust Optimization

Category 2: Stochastic Programming

Citation:

Download: [PDF]

Entry Submitted: 08/23/2019
Entry Accepted: 08/23/2019
Entry Last Modified: 08/23/2019

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