Bandits with concave rewards and convex knapsacks-Reference-Cited by-同舟云学术

Bandits with concave rewards and convex knapsacks

Published:2014-06 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the fifteenth ACM conference on Economics and computation
language:
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Author:

Agrawal Shipra¹,Devanur Nikhil R.¹

Affiliation:

1. Microsoft Research, Bangalore, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/2600057.2602844

Reference33 articles.

1. Yasin Abbasi-yadkori Dávid Pál and Csaba Szepesvári. 2012. Improved algorithms for linear stochastic bandits. In NIPS. Yasin Abbasi-yadkori Dávid Pál and Csaba Szepesvári. 2012. Improved algorithms for linear stochastic bandits. In NIPS.

2. Jacob Abernethy Peter L. Bartlett and Elad Hazan. 2011. Blackwell Approachability and Low-Regret Learning are Equivalent. In COLT. Jacob Abernethy Peter L. Bartlett and Elad Hazan. 2011. Blackwell Approachability and Low-Regret Learning are Equivalent. In COLT.

3. Shipra Agrawal and Nikhil R. Devanur. 2014. Bandits with concave rewards and convex knapsacks. CoRR abs/1402.5758 (2014). Shipra Agrawal and Nikhil R. Devanur. 2014. Bandits with concave rewards and convex knapsacks. CoRR abs/1402.5758 (2014).

4. Shipra Agrawal Zizhuo Wang and Yinyu Ye. 2009. A dynamic near-optimal algorithm for online linear programming. to appear in Operations Research; preprint arXiv:0911.2974 (2009). Shipra Agrawal Zizhuo Wang and Yinyu Ye. 2009. A dynamic near-optimal algorithm for online linear programming. to appear in Operations Research; preprint arXiv:0911.2974 (2009).

5. Peter Auer. 2003. Using Confidence Bounds for Exploitation-exploration Trade-offs. J. Mach. Learn. Res. 3 (March 2003) 397--422. Peter Auer. 2003. Using Confidence Bounds for Exploitation-exploration Trade-offs. J. Mach. Learn. Res. 3 (March 2003) 397--422.

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