Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning. Kuhn, D., Esfahani, P. M., Nguyen, V. A., & Shafieezadeh-Abadeh, S. In Netessine, S., Shier, D., & Greenberg, H. J., editors, Operations Research & Management Science in the Age of Analytics, pages 130–166. INFORMS, October, 2019.
Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning [link]Paper  doi  bibtex   
@incollection{netessine_wasserstein_2019,
	title = {Wasserstein {Distributionally} {Robust} {Optimization}: {Theory} and {Applications} in {Machine} {Learning}},
	isbn = {978-0-9906153-3-0},
	shorttitle = {Wasserstein {Distributionally} {Robust} {Optimization}},
	url = {http://pubsonline.informs.org/doi/10.1287/educ.2019.0198},
	language = {en},
	urldate = {2022-02-10},
	booktitle = {Operations {Research} \& {Management} {Science} in the {Age} of {Analytics}},
	publisher = {INFORMS},
	author = {Kuhn, Daniel and Esfahani, Peyman Mohajerin and Nguyen, Viet Anh and Shafieezadeh-Abadeh, Soroosh},
	editor = {Netessine, Serguei and Shier, Douglas and Greenberg, Harvey J.},
	month = oct,
	year = {2019},
	doi = {10.1287/educ.2019.0198},
	keywords = {/unread},
	pages = {130--166},
}

Downloads: 0