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 Shipra Agrawal

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Average citations per article4.33
Citation Count26
Publication count6
Publication years2013-2017
Available for download4
Average downloads per article153.00
Downloads (cumulative)612
Downloads (12 Months)269
Downloads (6 Weeks)34
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6 results found Export Results: bibtexendnoteacmrefcsv

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1 published by ACM
September 2017 Journal of the ACM (JACM): Volume 64 Issue 5, October 2017
Publisher: ACM
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 21,   Downloads (12 Months): 124,   Downloads (Overall): 124

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Thompson Sampling (TS) is one of the oldest heuristics for multiarmed bandit problems. It is a randomized algorithm based on Bayesian ideas and has recently generated significant interest after several studies demonstrated that it has favorable empirical performance compared to the state-of-the-art methods. In this article, a novel and almost ...
Keywords: Multi-armed bandits

2
December 2016 NIPS'16: Proceedings of the 30th International Conference on Neural Information Processing Systems
Publisher: Curran Associates Inc.
Bibliometrics:
Citation Count: 0
Downloads (6 Weeks): 0,   Downloads (12 Months): 0,   Downloads (Overall): 0

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We consider the linear contextual bandit problem with resource consumption, in addition to reward generation. In each round, the outcome of pulling an arm is a reward as well as a vector of resource consumptions. The expected values of these outcomes depend linearly on the context of that arm. The ...

3
January 2015 SODA '15: Proceedings of the twenty-sixth annual ACM-SIAM symposium on Discrete algorithms
Publisher: Society for Industrial and Applied Mathematics
Bibliometrics:
Citation Count: 10
Downloads (6 Weeks): 5,   Downloads (12 Months): 91,   Downloads (Overall): 231

Full text available: PDFPDF
We introduce the online stochastic Convex Programming (CP) problem, a very general version of stochastic online problems which allows arbitrary concave objectives and convex feasibility constraints. Many well-studied problems like online stochastic packing and covering, online stochastic matching with concave returns, etc. form a special case of online stochastic CP. ...

4
July 2014 AAAI'14: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence
Publisher: AAAI Press
Bibliometrics:
Citation Count: 2

Thompson Sampling (TS) has surged a lot of interest due to its good empirical performance, in particular in the computational advertising. Though successful, the tools for its performance analysis appeared only recently. In this paper, we describe and analyze SpectralTS algorithm for a bandit problem, where the payoffs of the ...

5 published by ACM
June 2014 EC '14: Proceedings of the fifteenth ACM conference on Economics and computation
Publisher: ACM
Bibliometrics:
Citation Count: 10
Downloads (6 Weeks): 8,   Downloads (12 Months): 54,   Downloads (Overall): 257

Full text available: PDFPDF
In this paper, we consider a very general model for exploration-exploitation tradeoff which allows arbitrary concave rewards and convex constraints on the decisions across time, in addition to the customary limitation on the time horizon. This model subsumes the classic multi-armed bandit (MAB) model, and the Bandits with Knapsacks (BwK) ...
Keywords: convex constraints, budgets, concave rewards, multi-armed bandits

6
June 2013 ICML'13: Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28
Publisher: JMLR.org
Bibliometrics:
Citation Count: 4

Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a randomized algorithm based on Bayesian ideas, and has recently generated significant interest after several studies demonstrated it to have better empirical performance compared to the state-of-the-art methods. However, many questions regarding its theoretical performance remained ...



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