Bandit and Limited-Feedback Problems

Investigators: Alexander (Sasha) Rakhlin

In this project we aim to understand theoretical guarantees of sequential decision-making with limited feedback. We also aim to apply our results in various domains, such as quantitative analysis of embedded systems.


Recent papers on the subject:

J. Abernethy and A. Rakhlin. Beating the Adaptive Bandit with High Probability, COLT 2009.

J. Abernethy, E. Hazan, A. Rakhlin. Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization, COLT 2008.

P. Bartlett, V. Dani, T. Hayes, S. Kakade, A. Rakhlin, and A. Tewari. High probability regret bounds for online optimization, COLT 2008.

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