Benjamin Van Roy

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Ian OsbandOpenAIEmail verificata su openai.com
John TsitsiklisProfessor of Electrical Engineering, MITEmail verificata su mit.edu
Zheng WenGoogle DeepMindEmail verificata su google.com
Daniel RussoColumbia UniversityEmail verificata su gsb.columbia.edu
Gabriel Y WeintraubStanford GSBEmail verificata su stanford.edu
Ciamac MoallemiProfessor, Graduate School of Business, Columbia UniversityEmail verificata su gsb.columbia.edu
Morteza IbrahimiStanford UniversityEmail verificata su stanford.edu
Paat RusmevichientongProfessor, Marshall School of Business, University of Southern CaliforniaEmail verificata su marshall.usc.edu
Vivek FariasMassachusetts Institute of TechnologyEmail verificata su mit.edu
Abbas KazerouniStanford UniversityEmail verificata su stanford.edu
Anant SAHAIEECS, University of California, BerkeleyEmail verificata su eecs.berkeley.edu
Alexander PritzelDeepmindEmail verificata su google.com
Charles BlundellResearch Scientist at DeepMindEmail verificata su google.com
Tsachy WeissmanProfessor of Electrical Engineering at Stanford UniversityEmail verificata su stanford.edu
Yi-Hao KaoPhD Candidate, Electrical Engineering, Stanford UniversityEmail verificata su stanford.edu
Hui ZhangCarnegie Mellon University, ConvivaEmail verificata su andrew.cmu.edu
Richard ZeckhauserHarvard UniversityEmail verificata su harvard.edu
Per EngeProfessor, Stanford UniversityEmail verificata su stanford.edu
Ramesh GovindanProfessor of Computer Science, University of Southern CaliforniaEmail verificata su usc.edu
Ashish GoelProfessor of Management Science and Engineering, and by courtesy, Computer Science, Stanford UniversityEmail verificata su stanford.edu

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Benjamin Van Roy

Stanford University

Email verificata su stanford.edu - Home page

reinforcement learning operations research information theory


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
Analysis of temporal-diffference learning with function approximation J Tsitsiklis, B Van Roy Advances in neural information processing systems 9, 1996	2272	1996
Deep exploration via bootstrapped DQN I Osband, C Blundell, A Pritzel, B Van Roy Advances in neural information processing systems 29, 2016	1550	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	1237	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	866	2001
Learning to optimize via posterior sampling D Russo, B Van Roy Mathematics of Operations Research 39 (4), 1221-1243, 2014	785	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	579	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	352	2019
Generalization and exploration via randomized value functions I Osband, B Van Roy, Z Wen International Conference on Machine Learning, 2377-2386, 2016	343	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	285	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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