
Shipra AgrawalAssistant ProfessorIndustrial Engineering and Operations Research Data Science Institute Columbia University 
S. Agrawal, V. Avandhanula, V. Goyal, A. Zeevi, "MNLBandit: A Dynamic Learning Approach to Assortment Selection". Under review.
S. Agrawal, N. R. Devanur, "Bandits with global convex constraints and objective". Under review.
S. Agrawal, N. Goyal, "Nearoptimal regret bounds for Thompson Sampling" Forthcoming in the Journal of ACM. [ pdf ].
T. Kocak, M. Valko, R. Munos, S. Agrawal, "Spectral Bandits for Smooth Graph Functions". Forthcoming in Journal of Machine Learning Research.
S. Agrawal, Z. Wang and Y. Ye, "A Dynamic NearOptimal Algorithm for Online Linear Programming". Operations Research 62:876890 (2014). [ EE ] [ arXiv ]
S. Agrawal, Y. Ding, A. Saberi, and Y. Ye, "Price of Correlations in Stochastic Optimization". Operations Research 60:243248 (2012). [ EE ]
S. Agrawal, E. Delage, M. Peters, Z. Wang, and Y. Ye, "A Unified Framework for Dynamic Prediction Market Design". Operations Research 59:3:550568 (2011). [ EE ] [ arXiv ]
S. Agrawal, N. Megiddo and B. Armbruster, "Equilibrium in Prediction Markets with Buyers and Sellers". Economic Letters 109:4649 (2010). [ EE ] [ pdf ]
S. Agrawal, J.R. Haritsa and B.A. Prakash, "FRAPP: A Framework for HighAccuracy PrivacyPreserving Mining". Data Mining and Knowledge Discovery Journal 18:101139 (2009). [ EE ] [ arXiv ]
S. Agrawal, C. N. Kanthi, K. V. M. Naidu, J. Ramamirtham, R. Rastogi, S. Satkin, and A. Srinivasan, Monitoring infrastructure for converged networks and services". Bell Labs Technical Journal 12(2): 6377 (2007). [ EE ]
S. Agrawal, R. Jia, "Posterior sampling for reinforcement learning: worstcase regret bounds". forthcoming in NIPS 2017 (spotlight).
S. Agrawal, V. Avandhanula, V. Goyal, A. Zeevi, "Thompson Sampling for MNLbandit". Conference on Learning Theory (COLT), 2017.
S. Agrawal, N. R. Devanur, "Linear Contextual Bandits with Knapsacks". NIPS 2016. [EE][ arXiv ]
S. Agrawal, V. Avandhanula, V. Goyal, A. Zeevi, "An ExplorationExploitation Approach for Assortment Selection". ACM conference on Eoconomics and Computation (EC) 2016. [ EE ][ pdf ]
S. Agrawal, N. R. Devanur, L. Li, "An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives". Conference on Learning Theory (COLT) 2016. [EE][arXiv ]
S. Agrawal, N. R. Devanur, "Fast algorithms for online stochastic convex programming". In Proceedings of the 21st Annual ACMSIAM Symposium on Discrete Algorithms (SODA), 2015. [ EE ] [ arXiv ]
S. Agrawal, N. R. Devanur, "Bandits with concave rewards and convex knapsacks". In Proceedings of the 15th ACM Conference on Electronic Commerce (EC), 2014. [ EE ] [ arXiv ]
T. Kocak, M. Valko and R. Munos, S. Agrawal, "Spectral Thompson Sampling". In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), 2014. [ pdf ]
S. Agrawal, N. Goyal, "Thompson Sampling for contextual bandits with linear payoffs". In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013. [ pdf ] [ arXiv ]
S. Agrawal, N. Goyal, "Further optimal regret bounds for Thompson Sampling", In Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS), 2013. [ pdf ] [ arXiv ]
S. Agrawal, N. Goyal, "Analysis of Thompson Sampling for the multiarmed bandit problem". In Proceedings of the 25th Annual Conference on Learning Theory (COLT), 2012. [ pdf ] [ arXiv ]
"Correlation Robust Stochastic Optimization". In Proceedings of the TwentyFirst Annual ACMSIAM Symposium on Discrete Algorithms (SODA), 2010. [ EE ] [ arXiv ]
"A Unified Framework for Dynamic Parimutuel Information Market Design". In Proceedings of the 10th ACM Conference on Electronic Commerce (EC), 2009. [ EE ]
S. Agrawal, Z. Wang and Y. Ye, "Parimutuel Betting on Permutations". In Proceedings of the 4th International Workshop On Internet And Network Economics (WINE), 2008. [ EE ] [ arXiv ]
S. Agrawal, K.V.M. Naidu and R. Rastogi, "Diagnosing LinkLevel Anomalies Using Passive Probes". In Proceedings of the 26th Annual IEEE Conference on Computer Communications (INFOCOM), 2007. [ EE ]
S. Agrawal, S. Deb, K.V.M. Naidu, and R. Rastogi, Efficient Detection of Distributed Constraint Violations". Short paper. In Proceedings of the 23rd International Conference on Data Engineering (ICDE), 2007. [ EE ]
S. Agrawal, P.P.S. Narayan, J. Ramamirtham, R. Rastogi, M. Smith, K. Swanson, and M. Thottan, "VoIP service quality monitoring using active and passive probes" . In Proceedings of the First International Conference on COM munication System softWAre and MiddlewaRE (COMSWARE), 2006. [ EE ]
S. Agrawal, J.R. Haritsa, "A Framework for HighAccuracy PrivacyPreserving Mining". In Proceedings of the 21st International Conference on Data Engineering (ICDE), 2005. [ EE ] [ arXiv ]
S. Agrawal, V. Krishnan and J.R. Haritsa, On Addressing Efficiency Concerns in PrivacyPreserving Mining". In Proceedings of the 9th International Conference on Database Systems for Advanced Applications (DASFAA), 2004. [ EE ] [ arXiv ]
Provost’s Grants Program for Junior Faculty who Contribute to the Diversity Goals of the University, Columbia University, Amount $25,000.00, 2016.
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