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Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs
[article]
2021
arXiv
pre-print
Despite a large amount of effort in dealing with heavy-tailed error in machine learning, little is known when moments of the error can become non-existential: the random noise η satisfies Pr[|η| > |y|] ≤ 1/|y|^α for some α > 0. We make the first attempt to actively handle such super heavy-tailed noise in bandit learning problems: We propose a novel robust statistical estimator, mean of medians, which estimates a random variable by computing the empirical mean of a sequence of empirical medians.
arXiv:2110.13876v1
fatcat:sj6t6frc3fhmjjpmidtupcn4f4