Benjamin Van Roy

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Ian OsbandDeepMindAdresse e-mail validée de google.com
John TsitsiklisProfessor of Electrical Engineering, MITAdresse e-mail validée de mit.edu
Zheng WenDeepMindAdresse e-mail validée de google.com
Daniel RussoColumbia UniversityAdresse e-mail validée de gsb.columbia.edu
Ciamac MoallemiProfessor, Graduate School of Business, Columbia UniversityAdresse e-mail validée de gsb.columbia.edu
Gabriel Y WeintraubStanford GSBAdresse e-mail validée de stanford.edu
Morteza IbrahimiStanford UniversityAdresse e-mail validée de stanford.edu
Paat RusmevichientongProfessor, Marshall School of Business, University of Southern CaliforniaAdresse e-mail validée de marshall.usc.edu
Vivek FariasMassachusetts Institute of TechnologyAdresse e-mail validée de mit.edu
Abbas KazerouniStanford UniversityAdresse e-mail validée de stanford.edu
Anant SAHAIEECS, University of California, BerkeleyAdresse e-mail validée de eecs.berkeley.edu
Alexander PritzelDeepmindAdresse e-mail validée de google.com
Charles BlundellResearch Scientist at DeepMindAdresse e-mail validée de google.com
Tsachy WeissmanProfessor of Electrical Engineering at Stanford UniversityAdresse e-mail validée de stanford.edu
Yi-Hao KaoPhD Candidate, Electrical Engineering, Stanford UniversityAdresse e-mail validée de stanford.edu
Hui ZhangCarnegie Mellon University, ConvivaAdresse e-mail validée de andrew.cmu.edu
Per EngeProfessor, Stanford UniversityAdresse e-mail validée de stanford.edu
Ramesh GovindanProfessor of Computer Science, University of Southern CaliforniaAdresse e-mail validée de usc.edu
Ashish GoelProfessor of Management Science and Engineering, and by courtesy, Computer Science, Stanford UniversityAdresse e-mail validée de stanford.edu
Paul CuffRenaissance TechnologiesAdresse e-mail validée de rentec.com

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Benjamin Van Roy

Stanford University

Adresse e-mail validée de stanford.edu - Page d'accueil

reinforcement learning operations research information theory


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Analysis of temporal-diffference learning with function approximation J Tsitsiklis, B Van Roy Advances in neural information processing systems 9, 1996	1972	1996
Deep exploration via bootstrapped DQN I Osband, C Blundell, A Pritzel, B Van Roy Advances in neural information processing systems 29, 2016	1189	2016
The linear programming approach to approximate dynamic programming DP De Farias, B Van Roy Operations research 51 (6), 850-865, 2003	912	2003
A tutorial on thompson sampling D Russo, B Van Roy, A Kazerouni, I Osband, Z Wen Foundations and Trends in Machine Learning 11 (1), pp. 1-96, 2018	839	2018
Regression methods for pricing complex American-style options JN Tsitsiklis, B Van Roy IEEE Transactions on Neural Networks 12 (4), 694-703, 2001	837	2001
Feature-based methods for large scale dynamic programming JN Tsitsiklis, B Van Roy Machine Learning 22 (1-3), 59-94, 1996	696	1996
Learning to optimize via posterior sampling D Russo, B Van Roy Mathematics of Operations Research 39 (4), 1221-1243, 2014	632	2014
Markov perfect industry dynamics with many firms G Weintraub, CL Benkard, B Van Roy Econometrica 76 (6), 1375-1411, 2008	522	2008
On constraint sampling in the linear programming approach to approximate dynamic programming DP De Farias, B Van Roy Mathematics of operations research 29 (3), 462-478, 2004	476	2004
Optimal stopping of Markov processes: Hilbert space theory, approximation algorithms, and an application to pricing high-dimensional financial derivatives JN Tsitsiklis, B Van Roy IEEE Transactions on Automatic Control 44 (10), 1840-1851, 1999	471	1999
An information-theoretic analysis of thompson sampling D Russo, B Van Roy The Journal of Machine Learning Research 17 (1), 2442-2471, 2016	358	2016
Consensus propagation CC Moallemi, B Van Roy IEEE Transactions on Information Theory 52 (11), 4753-4766, 2006	295	2006
Generalization and exploration via randomized value functions I Osband, B Van Roy, Z Wen International Conference on Machine Learning, 2377-2386, 2016	287	2016
Solving data mining problems through pattern recognition RL Kennedy, Y Lee, B Van Roy, CD Reed, RP Lippman Upper Saddle River, NJ: Prentice Hall PTR, 2011	266*	2011
Dynamic pricing with a prior on market response VF Farias, B Van Roy Operations Research 58 (1), 16-29, 2010	252	2010
Deep Exploration via Randomized Value Functions. I Osband, B Van Roy, DJ Russo, Z Wen J. Mach. Learn. Res. 20 (124), 1-62, 2019	249	2019
A neuro-dynamic programming approach to retailer inventory management B Van Roy, DP Bertsekas, Y Lee, JN Tsitsiklis Proceedings of the 36th IEEE Conference on Decision and Control 4, 4052-4057, 1997	222	1997
Average cost temporal-difference learning JN Tsitsiklis, B Van Roy Automatica 35, 319-349, 1999	217	1999
Why is posterior sampling better than optimism for reinforcement learning? I Osband, B Van Roy International conference on machine learning, 2701-2710, 2017	212	2017
Learning to optimize via information-directed sampling D Russo, B Van Roy Advances in Neural Information Processing Systems 27, 2014	194	2014

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