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0111 · Feb 202419922001200920172026
1 result for “Clipped-SGD-UCB”
Fast UCB-type algorithms for stochastic bandits with heavy and super heavy symmetric noisecs.LG

New UCB-type algorithms reduce regret bounds for stochastic bandits with heavy and super heavy noise.

problem Improving regret bounds for stochastic bandits with heavy-tailed noise.
method General convex optimization methods with an inexact oracle, Clipped-SGD-UCB algorithm.
result Achieved an O(log⁡TKTlog⁡T)O(\log T\sqrt{KT\log T}) regret bound for symmetric noise, better than general lower bounds.
UCB algorithmsstochastic banditsregret boundsheavy-tailed noiseClipped-SGD-UCB
2024-02-10abs ↗pdf ↗