| Analysis of thompson sampling for the multi-armed bandit problem S Agrawal, N Goyal Conference on learning theory, 39.1-39.26, 2012 | 838 | 2012 |
| Thompson sampling for contextual bandits with linear payoffs S Agrawal, N Goyal International Conference on Machine Learning, 127-135, 2013 | 558 | 2013 |
| Further optimal regret bounds for thompson sampling S Agrawal, N Goyal Artificial intelligence and statistics, 99-107, 2013 | 346 | 2013 |
| Further optimal regret bounds for thompson sampling S Agrawal, N Goyal Artificial intelligence and statistics, 99-107, 2013 | 346 | 2013 |
| A dynamic near-optimal algorithm for online linear programming S Agrawal, Z Wang, Y Ye Operations Research 62 (4), 876-890, 2014 | 230 | 2014 |
| A framework for high-accuracy privacy-preserving mining S Agrawal, JR Haritsa 21st International Conference on Data Engineering (ICDE'05), 193-204, 2005 | 217 | 2005 |
| Price of correlations in stochastic optimization S Agrawal, Y Ding, A Saberi, Y Ye Operations Research 60 (1), 150-162, 2012 | 115* | 2012 |
| Fast Algorithms for Online Stochastic Convex Programming S Agrawal, NR Devanur SODA 2015, 2015 | 112 | 2015 |
| Bandits with concave rewards and convex knapsacks S Agrawal, NR Devanur Proceedings of the fifteenth ACM conference on Economics and computation …, 2014 | 109 | 2014 |
| 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 | 109 | 2004 |
| Posterior sampling for reinforcement learning: worst-case regret bounds S Agrawal, R Jia arXiv preprint arXiv:1705.07041, 2017 | 107 | 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 | 102* | 2016 |
| 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 | 71 | 2007 |
| Thompson sampling for the mnl-bandit S Agrawal, V Avadhanula, V Goyal, A Zeevi Conference on Learning Theory, 76-78, 2017 | 61 | 2017 |
| Linear contextual bandits with knapsacks S Agrawal, NR Devanur arXiv preprint arXiv:1507.06738, 2015 | 58 | 2015 |
| 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 | 51* | 2011 |
| 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 | 44 | 2016 |
| 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 | 43 | 2009 |
| 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 | 42 | 2016 |
| Diagnosing link-level anomalies using passive probes S Agrawal, KVM Naidu, R Rastogi IEEE INFOCOM 2007-26th IEEE International Conference on Computer …, 2007 | 38 | 2007 |
Yinyu YeK.T. Li Professor of Engineering, Stanford UniversityVerified email at stanford.edu
