Shipra Agrawal is an Associate Professor of the Department of Industrial Engineering and Operations Research at Columbia University. She is also affiliated with the Department of Computer Science and the Data Science Institute. She received her PhD in Computer Science from Stanford University in June 2011 under the guidance of Prof. Yinyu Ye,
and was a researcher at Microsoft Research India from July 2011 to August 2015. Her research spans several areas of optimization and machine learning,
including online optimization under uncertainty, multi-armed bandits, online learning, and reinforcement learning. She is also interested in
prediction markets and game theory.
Shipra serves as an associate editor for Management Science
and Mathematics of Operations Research, INFORMS Journal on Optimization and Journal of Machine Learning Research (JMLR).
Her research is supported by a
Google Faculty research award (2017), Amazon research award (2017),
and an NSF CAREER Award.
For further information, please see CV .
Yunhao Tang (Graduated 2021, research scientist at Google Deepmind London)
Xingyu Zhang (Graduated 2021, research scientist at Amazon)
Randy Jia (Graduated 2020, research scientist at Amazon)
Mohammad Shadravan (Graduated 2020)
Vashist Avadhanula (Graduated 2018, research scientist at Facebook)
Selected Publications
(A full list of journal and conference papers is here )
Dynamic pricing and learning under Bass model, S. Agrawal, S. Yin, A. Zeevi, EC 2021. [arXiv]
Dynamic first price Auctions robust to heterogeneous buyers, S. Agrawal, E. Balkanski, V. Mirrokni, B. Sivan,
EC 2021. [arXiv]
On optimal ordering in the optimal stopping problem, S. Agrawal, J. Sethuraman, X. Zhang. EC 2020. [arXiv]
Reinforcement Learning for Integer Programming: Learning to Cut, Y. Tang, S. Agrawal, Y. Faenza, ICML 2020. [arXiv]
Learning in structured MDPs with convex cost functions: Improved regret bounds for inventory management. S. Agrawal and R. Jia, EC 2019. [arXiv]
Robust Repeated Auctions under Heterogeneous Buyer Behavior. S. Agrawal, C. Daskalakis, V. Mirrokni, B. Sivan, EC 2018. [ arXiv ]
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds. S. Agrawal and R. Jia, NIPS 2017 (spotlight) [ arXiv ]
Thompson Sampling for MNL-bandit. S. Agrawal, V. Avandhanula, V. Goyal, A. Zeevi. COLT 2017. [ arXiv ]
Fast algorithms for online stochastic convex programming. S. Agrawal, N. R. Devanur. SODA 2015. [ EE ] [ arXiv ]
Bandits with concave rewards and convex knapsacks. S. Agrawal, N. R. Devanur. EC 2014. [ EE ] [ arXiv ]
Near-Optimal Regret Bounds for Thompson Sampling JACM 64 Issue 5, October 2017 [ EE ][ pdf ].
(The pdf version here includes a correction made to the published version: in Lemma 2.13, page 15:16. Modified text is in blue)
Thompson Sampling for contextual bandits with linear payoffs. S. Agrawal, N. Goyal. ICML 2013. [ EE ]
[Newer version with improved results is here: arXiv ]
Analysis of Thompson Sampling for the multi-armed bandit problem. S. Agrawal, N. Goyal. COLT 2012. [ pdf ] [ arXiv ]
Associate Editor: Mathematics of Operations Research (Learning theory area), Management Science (Optimization area), INFORMS Journal on Optimization and Journal of Machine Learning Research (JMLR).
Mudd Building, Office 423
500 West 120th Street
New York, NY 10027
Videos
Title: A tutorial on multi-armed bandits at Simons Institute, Berkeley, CA. Aug 22, 2022.
Title: Dynamic Pricing and Learning under the Bass model, July 19, 2021.
Title: "Recent advances in multiarmed bandits for sequential decision making"
Tutorial in OR at at INFORMS annual meeting 2019
Title: Learning MDPs with convex cost functions: improved regret bounds for inventory management
at NeurIPS 2019 Workshop on Optimization foundations of Reinforcement Learning
( my talk begins at 37:33 )
News and Events
2022
Sep 12 Invited talk at IEOR Berkeley.
Aug 22 A tutorial on multi-armed bandits at Simons Institute, Berkeley, CA. Youtube video
Aug. 17 – Dec. 16, 2022 Co-organizing Fall'22 semester
on Data-Driven Decision Processes at Simons Institute, Berkeley, CA
June 25-26 Lecturer at IPCO'22 Summer school, Eindhoven, Netherlands
Oct 8 Keynote at RL workshop, Amazon Machine Learning Conference.
July 17/18 Invited talk at Theory of RL workshop at ICML 2020.
July 17/18 Invited talk on "Learning to manage inventory" at Real-World Experiment Design & Active Learning workshop at ICML 2020. Video is here (at 6:01:00))