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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
16182012
Thompson sampling for contextual bandits with linear payoffs
S Agrawal, N Goyal
International conference on machine learning, 127-135, 2013
12492013
Near-optimal regret bounds for thompson sampling
S Agrawal, N Goyal
Journal of the ACM (JACM) 64 (5), 1-24, 2017
6982017
A dynamic near-optimal algorithm for online linear programming
S Agrawal, Z Wang, Y Ye
Operations Research 62 (4), 876-890, 2014
3562014
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds
S Agrawal, R Jia
Advances in Neural Information Processing Systems 30, 2017
2512017
A framework for high-accuracy privacy-preserving mining
S Agrawal, JR Haritsa
21st International Conference on Data Engineering (ICDE'05), 193-204, 2005
2442005
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
240*2016
Bandits with concave rewards and convex knapsacks
S Agrawal, NR Devanur
Proceedings of the fifteenth ACM conference on Economics and computation …, 2014
2382014
Reinforcement learning for integer programming: Learning to cut
Y Tang, S Agrawal, Y Faenza
International conference on machine learning, 9367-9376, 2020
2322020
Fast Algorithms for Online Stochastic Convex Programming
S Agrawal, NR Devanur
SODA 2015, 2015
2012015
Price of correlations in stochastic optimization
S Agrawal, Y Ding, A Saberi, Y Ye
Operations Research 60 (1), 150-162, 2012
175*2012
Linear contextual bandits with knapsacks
S Agrawal, N Devanur
Advances in neural information processing systems 29, 2016
1742016
Bandits with delayed, aggregated anonymous feedback
C Pike-Burke, S Agrawal, C Szepesvari, S Grunewalder
International Conference on Machine Learning, 4105-4113, 2018
1402018
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
1392020
Thompson sampling for the mnl-bandit
S Agrawal, V Avadhanula, V Goyal, A Zeevi
Conference on learning theory, 76-78, 2017
1292017
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
1122016
On addressing efficiency concerns in privacy-preserving mining
S Agrawal, V Krishnan, JR Haritsa
Database Systems for Advanced Applications: 9th International Conference …, 2004
1112004
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
852019
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
762006
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
68*2011
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