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The Ethereal
Optimally Confident UCB: Improved Regret for Finite-Armed Bandits
July 28, 2015 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
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Authors
Tor Lattimore
arXiv ID
1507.07880
Category
cs.LG: Machine Learning
Cross-listed
math.OC
Citations
48
Venue
arXiv.org
Last Checked
6 months ago
Abstract
I present the first algorithm for stochastic finite-armed bandits that simultaneously enjoys order-optimal problem-dependent regret and worst-case regret. Besides the theoretical results, the new algorithm is simple, efficient and empirically superb. The approach is based on UCB, but with a carefully chosen confidence parameter that optimally balances the risk of failing confidence intervals against the cost of excessive optimism.
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