Shipra Agrawal

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Yinyu YeProfessor of Emeritus, Stanford UniversityDirección de correo verificada de stanford.edu
nikhil r. devanurAmazonDirección de correo verificada de nikhildevanur.com
Jayant HaritsaIndian Institute of ScienceDirección de correo verificada de iisc.ac.in
Zizhuo WangThe Chinese University of Hong Kong, Shenzhen / Cardinal OperationsDirección de correo verificada de cuhk.edu.cn
Rajeev RastogiAmazonDirección de correo verificada de amazon.com
Amin SaberiProfessor, Stanford UniversityDirección de correo verificada de stanford.edu
Vijay KrishnanIIT Bombay, Stanford University, https://turing.comDirección de correo verificada de cs.stanford.edu
Yichuan DingDesautels Faculty of Management, McGill UniversityDirección de correo verificada de mcgill.ca
Lihong Li (李力鸿)AmazonDirección de correo verificada de amazon.com
Tomáš KocákUniversity of PotsdamDirección de correo verificada de uni-potsdam.de
Michal ValkoChief Models Officer @ Stealth Startup, Inria & MVA - Ex: Llama at Meta; Gemini and BYOL @ DeepmindDirección de correo verificada de meta.com
Rémi MunosFAIR, MetaDirección de correo verificada de inria.fr
Erick DelageProfessor, Department of Decision Sciences, HEC MontréalDirección de correo verificada de hec.ca
Supratim DebTechnical Lead @ MetaDirección de correo verificada de fb.com
B. Aditya PrakashAssociate Professor, Georgia Institute of TechnologyDirección de correo verificada de cs.cmu.edu
Nimrod MegiddoDistinguished Research Staff Member, IBM Almaden Research CenterDirección de correo verificada de us.ibm.com

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Shipra Agrawal

Columbia university

Dirección de correo verificada de columbia.edu - Página principal

multi-armed bandits reinforcement learning online and stochastic optmization


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Analysis of thompson sampling for the multi-armed bandit problem S Agrawal, N Goyal Conference on learning theory, 39.1-39.26, 2012	1655	2012
Thompson sampling for contextual bandits with linear payoffs S Agrawal, N Goyal International conference on machine learning, 127-135, 2013	1281	2013
Near-optimal regret bounds for thompson sampling S Agrawal, N Goyal Journal of the ACM (JACM) 64 (5), 1-24, 2017	713	2017
A dynamic near-optimal algorithm for online linear programming S Agrawal, Z Wang, Y Ye Operations Research 62 (4), 876-890, 2014	368	2014
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds S Agrawal, R Jia Advances in Neural Information Processing Systems 30, 2017	255	2017
A near-optimal exploration-exploitation approach for assortment selection S Agrawal, V Avadhanula, V Goyal, A Zeevi Proceedings of the 2016 ACM Conference on Economics and Computation, 599-600, 2016	249*	2016
Reinforcement learning for integer programming: Learning to cut Y Tang, S Agrawal, Y Faenza International conference on machine learning, 9367-9376, 2020	247	2020
A framework for high-accuracy privacy-preserving mining S Agrawal, JR Haritsa 21st International Conference on Data Engineering (ICDE'05), 193-204, 2005	245	2005
Bandits with concave rewards and convex knapsacks S Agrawal, NR Devanur Proceedings of the fifteenth ACM conference on Economics and computation …, 2014	244	2014
Fast Algorithms for Online Stochastic Convex Programming S Agrawal, NR Devanur SODA 2015, 2015	205	2015
Linear contextual bandits with knapsacks S Agrawal, N Devanur Advances in neural information processing systems 29, 2016	179	2016
Price of correlations in stochastic optimization S Agrawal, Y Ding, A Saberi, Y Ye Operations Research 60 (1), 150-162, 2012	178*	2012
Discretizing continuous action space for on-policy optimization Y Tang, S Agrawal Proceedings of the aaai conference on artificial intelligence 34 (04), 5981-5988, 2020	150	2020
Bandits with delayed, aggregated anonymous feedback C Pike-Burke, S Agrawal, C Szepesvari, S Grunewalder International Conference on Machine Learning, 4105-4113, 2018	144	2018
Thompson sampling for the mnl-bandit S Agrawal, V Avadhanula, V Goyal, A Zeevi Conference on learning theory, 76-78, 2017	134	2017
An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives S Agrawal, NR Devanur, L Li Conference on Learning Theory, 4-18, 2016	114	2016
On addressing efficiency concerns in privacy-preserving mining S Agrawal, V Krishnan, JR Haritsa Database Systems for Advanced Applications: 9th International Conference …, 2004	111	2004
Learning in structured mdps with convex cost functions: Improved regret bounds for inventory management S Agrawal, R Jia Proceedings of the 2019 ACM Conference on Economics and Computation, 743-744, 2019	88	2019
Efficient detection of distributed constraint violations S Agrawal, S Deb, KVM Naidu, R Rastogi 2007 IEEE 23rd International Conference on Data Engineering, 1320-1324, 2006	77	2006
A unified framework for dynamic prediction market design S Agrawal, E Delage, M Peters, Z Wang, Y Ye Operations research 59 (3), 550-568, 2011	70*	2011

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