Daniel Kuhn Data Driven And Distributionally Robust Optimization And Applications Part 1 2

Daniel Kuhn: "Wasserstein Distributionally Robust Optimization: Theory And Applications In Machi ...

Daniel Kuhn: "Wasserstein Distributionally Robust Optimization: Theory And Applications In Machi ... In this paper we demonstrate that, under mild assumptions, the distributionally robust optimization problems over wasserstein balls can in fact be reformulated as finite convex programs in many interesting cases even as tractable linear programs. Daniel kuhn: data driven and distributionally robust optimization and applications part 1/2. speaker: daniel kuhn (epfl) event: dtu cee summer school 2018 on.

Daniel Kuhn - Wasserstein Distributionally Robust Optimization With Heterogeneous Data Sources ...

Daniel Kuhn - Wasserstein Distributionally Robust Optimization With Heterogeneous Data Sources ... In this paper we demonstrate that, under mild assumptions, the distributionally robust optimization problems over wasserstein balls can in fact be reformulated as finite convex programs—in many interesting cases even as tractable linear programs. Using the wasserstein metric, we construct a ball in the space of (multivariate and non discrete) probability distributions centered at the uniform distribution on the train ing samples, and we seek decisions that perform best in view of the worst case distribution within this wasserstein ball. ‪professor of operations research, epfl‬ ‪‪cited by 12,644‬‬ ‪stochastic programming‬ ‪robust optimization‬ ‪data driven optimization‬. In this paper we develop a method of data driven stochastic programming that avoids the arti cial decoupling of estimation and optimization and that chooses an estimator that adapts to the underlying optimization problem.

(PDF) Wasserstein Distributionally Robust Optimization: Theory And Applications In Machine Learning

(PDF) Wasserstein Distributionally Robust Optimization: Theory And Applications In Machine Learning ‪professor of operations research, epfl‬ ‪‪cited by 12,644‬‬ ‪stochastic programming‬ ‪robust optimization‬ ‪data driven optimization‬. In this paper we develop a method of data driven stochastic programming that avoids the arti cial decoupling of estimation and optimization and that chooses an estimator that adapts to the underlying optimization problem. In this paper we study distributionally robust optimization problems with a wasser stein ambiguity set centered at the uniform distribution pn on n independent and identically distributed training samples. Using the wasserstein metric, we construct a ball in the space of (multivariate and non discrete) probability distributions centered at the uniform distribution on the training samples, and we seek decisions that perform best in view of the worst case distribution within this wasser stein ball. In this paper we demonstrate that, under mild assumptions, the distributionally robust optimization problems over wasserstein balls can in fact be reformulated as finite convex programs in many interesting cases even as tractable linear programs. In this paper we demonstrate that, under mild assumptions, the distributionally robust optimization problems over wasserstein balls can in fact be reformulated as finite convex programs in.

2: Comparison Of Robust Optimization With Distributionally Robust... | Download Scientific Diagram

2: Comparison Of Robust Optimization With Distributionally Robust... | Download Scientific Diagram In this paper we study distributionally robust optimization problems with a wasser stein ambiguity set centered at the uniform distribution pn on n independent and identically distributed training samples. Using the wasserstein metric, we construct a ball in the space of (multivariate and non discrete) probability distributions centered at the uniform distribution on the training samples, and we seek decisions that perform best in view of the worst case distribution within this wasser stein ball. In this paper we demonstrate that, under mild assumptions, the distributionally robust optimization problems over wasserstein balls can in fact be reformulated as finite convex programs in many interesting cases even as tractable linear programs. In this paper we demonstrate that, under mild assumptions, the distributionally robust optimization problems over wasserstein balls can in fact be reformulated as finite convex programs in.

Daniel Kuhn: Data-driven and Distributionally Robust Optimization and Applications -- Part 1/2