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Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds Marek Petrik IBM T.J. Watson Research Center, Yorktown, NY, USA
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Document Date: 2012-06-07 13:19:56

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City

Yorktown / Edinburgh / /

Company

IBM / Princeton University Press / John Wiley & Sons Inc. / Neural Information Processing Systems / /

Country

Canada / United Kingdom / Scotland / /

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Facility

University of Alberta / Watson Research Center / /

Holiday

Assumption / /

IndustryTerm

action probabiliming algorithm / good solution / dual feasible solutions / basic feasible solutions / approximate solutions / simplicial branch-and-bound algorithm / /

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ALPs / /

Organization

Princeton University / Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds Marek Petrik IBM T.J. Watson Research Center / U.S. Securities and Exchange Commission / University of Alberta / /

Person

Dan Iancu / Dharmashankar Subramanian / Martin L. Markov / Laurent El / /

Position

author / /

ProgrammingLanguage

D / L / R / C / T / /

ProvinceOrState

Alberta / /

PublishedMedium

Annals of Mathematics / Machine Learning / Journal of Machine Learning Research / /

Technology

API algorithms / Av / action probabiliming algorithm / API / Machine Learning / simulation / ADP algorithms / simplicial branch-and-bound algorithm / /

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