Benjamin Van Roy

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Ian OsbandOpenAIVerified email at openai.com
John TsitsiklisProfessor of Electrical Engineering, MITVerified email at mit.edu
Zheng WenGoogle DeepMindVerified email at google.com
Daniel RussoColumbia UniversityVerified email at gsb.columbia.edu
Gabriel Y WeintraubStanford GSBVerified email at stanford.edu
Ciamac MoallemiProfessor, Graduate School of Business, Columbia UniversityVerified email at gsb.columbia.edu
Morteza IbrahimiStanford UniversityVerified email at stanford.edu
Paat RusmevichientongProfessor, Marshall School of Business, University of Southern CaliforniaVerified email at marshall.usc.edu
Vivek FariasMassachusetts Institute of TechnologyVerified email at mit.edu
Abbas KazerouniStanford UniversityVerified email at stanford.edu
Anant SAHAIEECS, University of California, BerkeleyVerified email at eecs.berkeley.edu
Alexander PritzelDeepmindVerified email at google.com
Charles BlundellResearch Scientist at DeepMindVerified email at google.com
Tsachy WeissmanProfessor of Electrical Engineering at Stanford UniversityVerified email at stanford.edu
Yi-Hao KaoPhD Candidate, Electrical Engineering, Stanford UniversityVerified email at stanford.edu
Hui ZhangCarnegie Mellon University, ConvivaVerified email at andrew.cmu.edu
Richard ZeckhauserHarvard UniversityVerified email at harvard.edu
Per EngeProfessor, Stanford UniversityVerified email at stanford.edu
Ramesh GovindanProfessor of Computer Science, University of Southern CaliforniaVerified email at usc.edu
Ashish GoelProfessor of Management Science and Engineering, and by courtesy, Computer Science, Stanford UniversityVerified email at stanford.edu

Benjamin Van Roy

Stanford University

Verified email at stanford.edu - Homepage

reinforcement learning operations research information theory


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Analysis of temporal-diffference learning with function approximation J Tsitsiklis, B Van Roy Advances in neural information processing systems 9, 1996	2243	1996
Deep exploration via bootstrapped DQN I Osband, C Blundell, A Pritzel, B Van Roy Advances in neural information processing systems 29, 2016	1501	2016
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	1189	2018
The linear programming approach to approximate dynamic programming DP De Farias, B Van Roy Operations research 51 (6), 850-865, 2003	976	2003
Regression methods for pricing complex American-style options JN Tsitsiklis, B Van Roy IEEE Transactions on Neural Networks 12 (4), 694-703, 2001	864	2001
Learning to optimize via posterior sampling D Russo, B Van Roy Mathematics of Operations Research 39 (4), 1221-1243, 2014	764	2014
Feature-based methods for large scale dynamic programming JN Tsitsiklis, B Van Roy Machine Learning 22 (1), 59-94, 1996	724	1996
Markov perfect industry dynamics with many firms G Weintraub, CL Benkard, B Van Roy Econometrica 76 (6), 1375-1411, 2008	570	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	497	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	490	1999
An information-theoretic analysis of thompson sampling D Russo, B Van Roy Journal of Machine Learning Research 17 (68), 1-30, 2016	433	2016
Deep Exploration via Randomized Value Functions. I Osband, B Van Roy, DJ Russo, Z Wen The Journal of Machine Learning Research 20 (124), 1-62, 2019	342	2019
Generalization and exploration via randomized value functions I Osband, B Van Roy, Z Wen International Conference on Machine Learning, 2377-2386, 2016	338	2016
Consensus propagation CC Moallemi, B Van Roy IEEE Transactions on Information Theory 52 (11), 4753-4766, 2006	307	2006
Eluder dimension and the sample complexity of optimistic exploration D Russo, B Van Roy Advances in Neural Information Processing Systems 26, 2013	273	2013
Why is posterior sampling better than optimism for reinforcement learning? I Osband, B Van Roy International conference on machine learning, 2701-2710, 2017	271	2017
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	270*	2011
Dynamic pricing with a prior on market response VF Farias, B Van Roy Operations Research 58 (1), 16-29, 2010	270	2010
Learning to optimize via information-directed sampling D Russo, B Van Roy Advances in neural information processing systems 27, 2014	243	2014
Average cost temporal-difference learning JN Tsitsiklis, B Van Roy Automatica 35, 319-349, 1999	240	1999

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