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The Ethereal
A minimax and asymptotically optimal algorithm for stochastic bandits
February 23, 2017 ยท Declared Dead ยท ๐ International Conference on Algorithmic Learning Theory
"No code URL or promise found in abstract"
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Authors
Pierre Mรฉnard, Aurรฉlien Garivier
arXiv ID
1702.07211
Category
stat.ML: Machine Learning (Stat)
Cross-listed
cs.LG,
math.ST
Citations
63
Venue
International Conference on Algorithmic Learning Theory
Last Checked
6 months ago
Abstract
We propose the kl-UCB ++ algorithm for regret minimization in stochastic bandit models with exponential families of distributions. We prove that it is simultaneously asymptotically optimal (in the sense of Lai and Robbins' lower bound) and minimax optimal. This is the first algorithm proved to enjoy these two properties at the same time. This work thus merges two different lines of research with simple and clear proofs.
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