Shipra Agrawal
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Analysis of thompson sampling for the multi-armed bandit problem
S Agrawal, N Goyal
Conference on learning theory, 39.1-39.26, 2012
9602012
Thompson sampling for contextual bandits with linear payoffs
S Agrawal, N Goyal
International Conference on Machine Learning, 127-135, 2013
6832013
Further optimal regret bounds for thompson sampling
S Agrawal, N Goyal
Artificial intelligence and statistics, 99-107, 2013
4142013
Further optimal regret bounds for thompson sampling
S Agrawal, N Goyal
Artificial intelligence and statistics, 99-107, 2013
4142013
A dynamic near-optimal algorithm for online linear programming
S Agrawal, Z Wang, Y Ye
Operations Research 62 (4), 876-890, 2014
2532014
A framework for high-accuracy privacy-preserving mining
S Agrawal, JR Haritsa
21st International Conference on Data Engineering (ICDE'05), 193-204, 2005
2242005
Bandits with concave rewards and convex knapsacks
S Agrawal, NR Devanur
Proceedings of the fifteenth ACM conference on Economics and computation …, 2014
1382014
Posterior sampling for reinforcement learning: worst-case regret bounds
S Agrawal, R Jia
Advances in Neural Information Processing Systems, 1184-1194, 2017
1312017
Fast Algorithms for Online Stochastic Convex Programming
S Agrawal, NR Devanur
SODA 2015, 2015
1252015
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
121*2016
Price of correlations in stochastic optimization
S Agrawal, Y Ding, A Saberi, Y Ye
Operations Research 60 (1), 150-162, 2012
119*2012
On addressing efficiency concerns in privacy-preserving mining
S Agrawal, V Krishnan, JR Haritsa
International Conference on Database Systems for Advanced Applications, 113-124, 2004
1102004
Linear contextual bandits with knapsacks
S Agrawal, N Devanur
Advances in Neural Information Processing Systems 29, 3450-3458, 2016
782016
Thompson sampling for the mnl-bandit
S Agrawal, V Avadhanula, V Goyal, A Zeevi
Conference on Learning Theory, 76-78, 2017
752017
Efficient detection of distributed constraint violations
S Agrawal, S Deb, KVM Naidu, R Rastogi
2007 IEEE 23rd International Conference on Data Engineering, 1320-1324, 2007
732007
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
562016
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
55*2011
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
492016
Bandits with delayed, aggregated anonymous feedback
C Pike-Burke, S Agrawal, C Szepesvari, S Grunewalder
International Conference on Machine Learning, 4105-4113, 2018
46*2018
FRAPP: A framework for high-accuracy privacy-preserving mining
S Agrawal, JR Haritsa, BA Prakash
Data Mining and Knowledge Discovery 18 (1), 101-139, 2009
432009
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Artikler 1–20