Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning-Reference-Cited by-同舟云学术

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning

Published:2019-10 Issue: Volume: Page:130-166
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Container-title:Operations Research & Management Science in the Age of Analytics
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Author:

Kuhn Daniel¹,Esfahani Peyman Mohajerin²,Nguyen Viet Anh¹,Shafieezadeh-Abadeh Soroosh³

Affiliation:

1. College of Management of Technology, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland;

2. Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, Netherlands;

3. College of Management of Technology, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

Publisher

INFORMS

Reference97 articles.

1. D. Alvarez-Melis, T. Jaakkola, and S. Jegelka. Structured optimal transport.Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, 1771–1780, 2018.

2. M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein generative adversarial networks.Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research 214–223, 2017.

3. Iterative Bregman Projections for Regularized Transportation Problems

4. Deriving robust counterparts of nonlinear uncertain inequalities

5. A new algorithm for the assignment problem

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