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ORCIDShipra Agrawal 0001
Person information
- affiliation: Columbia University, New York City, NY, USA
- affiliation (2011 - 2015): Microsoft Research India, Bangalore, India
- affiliation (PhD 2011): Stanford University, CA, USA
Other persons with the same name
- Shipra Agrawal 0002 — Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
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2020 – today
- 2024
[e2]Shipra Agrawal, Aaron Roth:
The Thirty Seventh Annual Conference on Learning Theory, June 30 - July 3, 2023, Edmonton, Canada. Proceedings of Machine Learning Research 247, PMLR 2024 [contents]- [i33]Shipra Agrawal, Wei Tang:
Dynamic Pricing and Learning with Long-term Reference Effects. CoRR abs/2402.12562 (2024) - [i32]Priyank Agrawal, Shipra Agrawal:
Optimistic Q-learning for average reward and episodic reinforcement learning. CoRR abs/2407.13743 (2024) - 2023
- [j10]Shipra Agrawal
, Randy Jia:
Optimistic Posterior Sampling for Reinforcement Learning: Worst-Case Regret Bounds. Math. Oper. Res. 48(1): 363-392 (2023) - [c34]Shipra Agrawal, Yiding Feng, Wei Tang:
Dynamic Pricing and Learning with Bayesian Persuasion. NeurIPS 2023 - [e1]Shipra Agrawal, Francesco Orabona:
International Conference on Algorithmic Learning Theory, February 20-23, 2023, Singapore. Proceedings of Machine Learning Research 201, PMLR 2023 [contents] - [i31]Shipra Agrawal, Yiding Feng, Wei Tang:
Dynamic Pricing and Learning with Bayesian Persuasion. CoRR abs/2304.14385 (2023) - 2022
- [j9]Shipra Agrawal, Randy Jia:
Learning in Structured MDPs with Convex Cost Functions: Improved Regret Bounds for Inventory Management. Oper. Res. 70(3): 1646-1664 (2022) - [c33]Sudeep Raja Putta, Shipra Agrawal:
Scale-Free Adversarial Multi Armed Bandits. ALT 2022: 910-930 - [c32]Steven Yin, Shipra Agrawal, Assaf Zeevi:
Online Allocation and Learning in the Presence of Strategic Agents. NeurIPS 2022 - [i30]Steven Yin, Shipra Agrawal, Assaf Zeevi:
Online Allocation and Learning in the Presence of Strategic Agents. CoRR abs/2209.12112 (2022) - 2021
- [c31]Shipra Agrawal, Steven Yin, Assaf Zeevi:
Dynamic Pricing and Learning under the Bass Model. EC 2021: 2-3 - [c30]Shipra Agrawal, Eric Balkanski, Vahab S. Mirrokni, Balasubramanian Sivan:
Robust Repeated First Price Auctions. EC 2021: 4 - [i29]Shipra Agrawal, Steven Yin, Assaf Zeevi:
Dynamic Pricing and Learning under the Bass Model. CoRR abs/2103.05199 (2021) - [i28]Sudeep Raja Putta, Shipra Agrawal:
Scale Free Adversarial Multi Armed Bandits. CoRR abs/2106.04700 (2021) - 2020
- [c29]Yunhao Tang, Shipra Agrawal:
Discretizing Continuous Action Space for On-Policy Optimization. AAAI 2020: 5981-5988 - [c28]Yunhao Tang, Shipra Agrawal, Yuri Faenza:
Reinforcement Learning for Integer Programming: Learning to Cut. ICML 2020: 9367-9376 - [c27]Shipra Agrawal, Jay Sethuraman, Xingyu Zhang:
On Optimal Ordering in the Optimal Stopping Problem. EC 2020: 187-188
2010 – 2019
- 2019
- [j8]Shipra Agrawal, Vashist Avadhanula, Vineet Goyal, Assaf Zeevi:
MNL-Bandit: A Dynamic Learning Approach to Assortment Selection. Oper. Res. 67(5): 1453-1485 (2019) - [j7]Shipra Agrawal, Nikhil R. Devanur:
Bandits with Global Convex Constraints and Objective. Oper. Res. 67(5): 1486-1502 (2019) - [c26]Shipra Agrawal, Randy Jia:
Learning in Structured MDPs with Convex Cost Functions: Improved Regret Bounds for Inventory Management. EC 2019: 743-744 - [c25]Shipra Agrawal, Mohammad Shadravan, Cliff Stein:
Submodular Secretary Problem with Shortlists. ITCS 2019: 1:1-1:19 - [i27]Yunhao Tang, Shipra Agrawal:
Discretizing Continuous Action Space for On-Policy Optimization. CoRR abs/1901.10500 (2019) - [i26]Shipra Agrawal, Randy Jia:
Learning in structured MDPs with convex cost functions: Improved regret bounds for inventory management. CoRR abs/1905.04337 (2019) - [i25]Shipra Agrawal, Eric Balkanski, Vahab S. Mirrokni, Balasubramanian Sivan:
Dynamic First Price Auctions Robust to Heterogeneous Buyers. CoRR abs/1906.03286 (2019) - [i24]Yunhao Tang, Shipra Agrawal, Yuri Faenza:
Reinforcement Learning for Integer Programming: Learning to Cut. CoRR abs/1906.04859 (2019) - [i23]Shipra Agrawal, Jay Sethuraman, Xingyu Zhang:
On optimal ordering in the optimal stopping problem. CoRR abs/1911.05096 (2019) - 2018
- [c24]Shipra Agrawal, Morteza Zadimoghaddam, Vahab S. Mirrokni:
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy. ICML 2018: 99-108 - [c23]Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvári, Steffen Grünewälder:
Bandits with Delayed, Aggregated Anonymous Feedback. ICML 2018: 4102-4110 - [c22]Yunhao Tang, Shipra Agrawal:
Exploration by Distributional Reinforcement Learning. IJCAI 2018: 2710-2716 - [c21]Shipra Agrawal, Constantinos Daskalakis, Vahab S. Mirrokni, Balasubramanian Sivan:
Robust Repeated Auctions under Heterogeneous Buyer Behavior. EC 2018: 171 - [i22]Shipra Agrawal, Constantinos Daskalakis, Vahab S. Mirrokni, Balasubramanian Sivan:
Robust Repeated Auctions under Heterogeneous Buyer Behavior. CoRR abs/1803.00494 (2018) - [i21]Yunhao Tang, Shipra Agrawal:
Exploration by Distributional Reinforcement Learning. CoRR abs/1805.01907 (2018) - [i20]Yunhao Tang, Shipra Agrawal:
Implicit Policy for Reinforcement Learning. CoRR abs/1806.06798 (2018) - [i19]Shipra Agrawal, Mohammad Shadravan, Cliff Stein:
Submodular Secretary Problem with Shortlists. CoRR abs/1809.05082 (2018) - [i18]Yunhao Tang, Shipra Agrawal:
Boosting Trust Region Policy Optimization by Normalizing Flows Policy. CoRR abs/1809.10326 (2018) - 2017
- [j6]Shipra Agrawal, Navin Goyal:
Near-Optimal Regret Bounds for Thompson Sampling. J. ACM 64(5): 30:1-30:24 (2017) - [c20]Shipra Agrawal, Vashist Avadhanula, Vineet Goyal, Assaf Zeevi:
Thompson Sampling for the MNL-Bandit. COLT 2017: 76-78 - [c19]Shipra Agrawal, Randy Jia:
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds. NIPS 2017: 1184-1194 - [i17]Shipra Agrawal, Randy Jia:
Posterior sampling for reinforcement learning: worst-case regret bounds. CoRR abs/1705.07041 (2017) - [i16]Shipra Agrawal, Vashist Avadhanula, Vineet Goyal, Assaf Zeevi:
Thompson Sampling for the MNL-Bandit. CoRR abs/1706.00977 (2017) - [i15]Shipra Agrawal, Vashist Avadhanula, Vineet Goyal, Assaf Zeevi:
MNL-Bandit: A Dynamic Learning Approach to Assortment Selection. CoRR abs/1706.03880 (2017) - [i14]Ciara Pike-Burke, Shipra Agrawal, Csaba Szepesvári, Steffen Grünewälder:
Bandits with Delayed Anonymous Feedback. CoRR abs/1709.06853 (2017) - 2016
- [c18]Shipra Agrawal, Nikhil R. Devanur, Lihong Li:
An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives. COLT 2016: 4-18 - [c17]Shipra Agrawal, Nikhil R. Devanur:
Linear Contextual Bandits with Knapsacks. NIPS 2016: 3450-3458 - [c16]Shipra Agrawal, Vashist Avadhanula, Vineet Goyal, Assaf Zeevi:
A Near-Optimal Exploration-Exploitation Approach for Assortment Selection. EC 2016: 599-600 - 2015
- [c15]Shipra Agrawal, Nikhil R. Devanur:
Fast Algorithms for Online Stochastic Convex Programming. SODA 2015: 1405-1424 - [i13]Shipra Agrawal, Nikhil R. Devanur, Lihong Li:
Contextual Bandits with Global Constraints and Objective. CoRR abs/1506.03374 (2015) - [i12]Shipra Agrawal, Nikhil R. Devanur:
Linear Contextual Bandits with Global Constraints and Objective. CoRR abs/1507.06738 (2015) - 2014
- [j5]Shipra Agrawal, Zizhuo Wang, Yinyu Ye:
A Dynamic Near-Optimal Algorithm for Online Linear Programming. Oper. Res. 62(4): 876-890 (2014) - [c14]Tomás Kocák, Michal Valko, Rémi Munos, Shipra Agrawal:
Spectral Thompson Sampling. AAAI 2014: 1911-1917 - [c13]Shipra Agrawal, Nikhil R. Devanur:
Bandits with concave rewards and convex knapsacks. EC 2014: 989-1006 - [i11]Shipra Agrawal, Nikhil R. Devanur:
Bandits with concave rewards and convex knapsacks. CoRR abs/1402.5758 (2014) - [i10]Shipra Agrawal, Nikhil R. Devanur:
Fast Algorithms for Online Stochastic Convex Programming. CoRR abs/1410.7596 (2014) - 2013
- [c12]Shipra Agrawal, Navin Goyal:
Further Optimal Regret Bounds for Thompson Sampling. AISTATS 2013: 99-107 - [c11]Shipra Agrawal, Navin Goyal:
Thompson Sampling for Contextual Bandits with Linear Payoffs. ICML (3) 2013: 127-135 - 2012
- [j4]Shipra Agrawal, Yichuan Ding, Amin Saberi, Yinyu Ye:
Price of Correlations in Stochastic Optimization. Oper. Res. 60(1): 150-162 (2012) - [c10]Shipra Agrawal, Navin Goyal:
Analysis of Thompson Sampling for the Multi-armed Bandit Problem. COLT 2012: 39.1-39.26 - [i9]Shipra Agrawal, Navin Goyal:
Thompson Sampling for Contextual Bandits with Linear Payoffs. CoRR abs/1209.3352 (2012) - [i8]Shipra Agrawal, Navin Goyal:
Further Optimal Regret Bounds for Thompson Sampling. CoRR abs/1209.3353 (2012) - 2011
- [b1]Shipra Agrawal:
Optimization under uncertainty: bounding the correlation gap. Stanford University, USA, 2011 - [j3]Shipra Agrawal, Erick Delage, Mark Peters, Zizhuo Wang, Yinyu Ye:
A Unified Framework for Dynamic Prediction Market Design. Oper. Res. 59(3): 550-568 (2011) - [i7]Shipra Agrawal, Navin Goyal:
Analysis of Thompson Sampling for the multi-armed bandit problem. CoRR abs/1111.1797 (2011) - 2010
- [c9]Shipra Agrawal, Yichuan Ding, Amin Saberi, Yinyu Ye:
Correlation Robust Stochastic Optimization. SODA 2010: 1087-1096
2000 – 2009
- 2009
- [j2]Shipra Agrawal, Jayant R. Haritsa, B. Aditya Prakash:
FRAPP: a framework for high-accuracy privacy-preserving mining. Data Min. Knowl. Discov. 18(1): 101-139 (2009) - [c8]Shipra Agrawal, Erick Delage, Mark Peters, Zizhuo Wang, Yinyu Ye:
A unified framework for dynamic pari-mutuel information market design. EC 2009: 255-264 - [i6]Shipra Agrawal, Yichuan Ding, Amin Saberi, Yinyu Ye:
Distributionally Robust Stochastic Programming with Binary Random Variables. CoRR abs/0902.1792 (2009) - [i5]Shipra Agrawal, Zizhuo Wang, Yinyu Ye:
A Dynamic Near-Optimal Algorithm for Online Linear Programming. CoRR abs/0911.2974 (2009) - 2008
- [c7]Shipra Agrawal, Zizhuo Wang, Yinyu Ye:
Parimutuel Betting on Permutations. WINE 2008: 126-137 - [i4]Shipra Agrawal, Zizhuo Wang, Yinyu Ye:
Parimutuel Betting on Permutations. CoRR abs/0804.2288 (2008) - [i3]Shipra Agrawal, Amin Saberi, Yinyu Ye:
Stochastic Combinatorial Optimization under Probabilistic Constraints. CoRR abs/0809.0460 (2008) - 2007
- [j1]Shipra Agrawal, C. N. Kanthi, K. V. M. Naidu, Jeyashankher Ramamirtham, Rajeev Rastogi, Scott Satkin, Anand Srinivasan:
Monitoring infrastructure for converged networks and services. Bell Labs Tech. J. 12(2): 63-77 (2007) - [c6]Shipra Agrawal, Supratim Deb, K. V. M. Naidu, Rajeev Rastogi:
Efficient Detection of Distributed Constraint Violations. ICDE 2007: 1320-1324 - [c5]Shipra Agrawal, K. V. M. Naidu, Rajeev Rastogi:
Diagnosing Link-Level Anomalies Using Passive Probes. INFOCOM 2007: 1757-1765 - 2006
- [c4]Shipra Agrawal, Supratim Deb, K. V. M. Naidu, Rajeev Rastogi:
Efficient Detection of Distributed Constraint Violations. COMAD 2006: 230-239 - [c3]Shipra Agrawal, P. P. S. Narayan, Jeyashankher Ramamirtham, Rajeev Rastogi, Mark A. Smith, Ken Swanson, Marina Thottan:
VoIP service quality monitoring using active and passive probes. COMSWARE 2006: 1-10 - 2005
- [c2]Shipra Agrawal, Jayant R. Haritsa:
A Framework for High-Accuracy Privacy-Preserving Mining. ICDE 2005: 193-204 - 2004
- [c1]Shipra Agrawal, Vijay Krishnan, Jayant R. Haritsa:
On Addressing Efficiency Concerns in Privacy-Preserving Mining. DASFAA 2004: 113-124 - [i2]Shipra Agrawal, Jayant R. Haritsa:
A Framework for High-Accuracy Privacy-Preserving Mining. CoRR cs.DB/0407035 (2004) - 2003
- [i1]Shipra Agrawal, Vijay Krishnan, Jayant R. Haritsa:
On Addressing Efficiency Concerns in Privacy Preserving Data Mining. CoRR cs.DB/0310038 (2003)
Coauthor Index
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