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This paper proposes a novel UCB-type algorithm, M-LCB, designed to manage a pool of self-learning experts in a stochastic environment while accounting for a limited per-round learning budget and derives anytime regret bounds for M-LCB that scale with the individual regrets of the experts.
This work derives a finite-sample comparison of clipped and unclipped Polyak-Ruppert averaged SGD under finite conditional $p$-th moments, and gives explicit accuracy and confidence conditions under which, for $p>2$, the Gaussian term dominates the unclipped deviation bound, so clipping need not improve its leading ord...
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