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Revisiting Concentration of Missing Mass

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arxiv 2005.10018 v3 pith:3VFMJY2T submitted 2020-05-19 math.ST cs.LGmath.PRstat.MLstat.TH

Maciej Skorski This is my paper · ORCID
classification math.STcs.LGmath.PRstat.MLstat.TH
keywords concentrationmassmissingben-hamouboundscelebrateddevelopingemph
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We revisit the problem of \emph{missing mass concentration}, developing a new method of estimating concentration of heterogenic sums, in spirit of celebrated Rosenthal's inequality. As a result we slightly improve the state-of-art bounds due to Ben-Hamou at al., and simplify the proofs.

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  1. Gradient-free stochastic optimization of derivatives under strong convexity

    math.ST 2026-07 accept novelty 7.0 of 10

    The minimax optimal rate for minimizing the k-th derivative of a Hölder function from noisy zero-order queries is N^{-(β-1)/(β+k)}, achieved by a kernel-based projected stochastic gradient algorithm.

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