Benjamin Van Roy

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Ian OsbandOpenAIE-mail confirmado em openai.com
John TsitsiklisProfessor of Electrical Engineering, MITE-mail confirmado em mit.edu
Zheng WenGoogle DeepMindE-mail confirmado em google.com
Daniel RussoColumbia UniversityE-mail confirmado em gsb.columbia.edu
Gabriel Y WeintraubStanford GSBE-mail confirmado em stanford.edu
Ciamac MoallemiProfessor, Graduate School of Business, Columbia UniversityE-mail confirmado em gsb.columbia.edu
Morteza IbrahimiStanford UniversityE-mail confirmado em stanford.edu
Paat RusmevichientongProfessor, Marshall School of Business, University of Southern CaliforniaE-mail confirmado em marshall.usc.edu
Vivek FariasMassachusetts Institute of TechnologyE-mail confirmado em mit.edu
Abbas KazerouniStanford UniversityE-mail confirmado em stanford.edu
Anant SAHAIEECS, University of California, BerkeleyE-mail confirmado em eecs.berkeley.edu
Alexander PritzelDeepmindE-mail confirmado em google.com
Charles BlundellResearch Scientist at DeepMindE-mail confirmado em google.com
Tsachy WeissmanProfessor of Electrical Engineering at Stanford UniversityE-mail confirmado em stanford.edu
Yi-Hao KaoPhD Candidate, Electrical Engineering, Stanford UniversityE-mail confirmado em stanford.edu
Hui ZhangCarnegie Mellon University, ConvivaE-mail confirmado em andrew.cmu.edu
Richard ZeckhauserHarvard UniversityE-mail confirmado em harvard.edu
Per EngeProfessor, Stanford UniversityE-mail confirmado em stanford.edu
Ramesh GovindanProfessor of Computer Science, University of Southern CaliforniaE-mail confirmado em usc.edu
Ashish GoelProfessor of Management Science and Engineering, and by courtesy, Computer Science, Stanford UniversityE-mail confirmado em stanford.edu

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Benjamin Van Roy

Stanford University

E-mail confirmado em stanford.edu - Página inicial

reinforcement learning operations research information theory


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
Analysis of temporal-diffference learning with function approximation J Tsitsiklis, B Van Roy Advances in neural information processing systems 9, 1996	2274	1996
Deep exploration via bootstrapped DQN I Osband, C Blundell, A Pritzel, B Van Roy Advances in neural information processing systems 29, 2016	1552	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	1238	2018
The linear programming approach to approximate dynamic programming DP De Farias, B Van Roy Operations research 51 (6), 850-865, 2003	982	2003
Regression methods for pricing complex American-style options JN Tsitsiklis, B Van Roy IEEE Transactions on Neural Networks 12 (4), 694-703, 2001	867	2001
Learning to optimize via posterior sampling D Russo, B Van Roy Mathematics of Operations Research 39 (4), 1221-1243, 2014	786	2014
Feature-based methods for large scale dynamic programming JN Tsitsiklis, B Van Roy Machine Learning 22 (1), 59-94, 1996	728	1996
Markov perfect industry dynamics with many firms G Weintraub, CL Benkard, B Van Roy Econometrica 76 (6), 1375-1411, 2008	581	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	502	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	491	1999
An information-theoretic analysis of thompson sampling D Russo, B Van Roy Journal of Machine Learning Research 17 (68), 1-30, 2016	451	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	354	2019
Generalization and exploration via randomized value functions I Osband, B Van Roy, Z Wen International Conference on Machine Learning, 2377-2386, 2016	345	2016
Consensus propagation CC Moallemi, B Van Roy IEEE Transactions on Information Theory 52 (11), 4753-4766, 2006	308	2006
Eluder dimension and the sample complexity of optimistic exploration D Russo, B Van Roy Advances in Neural Information Processing Systems 26, 2013	286	2013
Why is posterior sampling better than optimism for reinforcement learning? I Osband, B Van Roy International conference on machine learning, 2701-2710, 2017	278	2017
Dynamic pricing with a prior on market response VF Farias, B Van Roy Operations Research 58 (1), 16-29, 2010	274	2010
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
Learning to optimize via information-directed sampling D Russo, B Van Roy Advances in neural information processing systems 27, 2014	251	2014
Average cost temporal-difference learning JN Tsitsiklis, B Van Roy Automatica 35, 319-349, 1999	241	1999

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