Tight Chernoff-Like Bounds Under Limited Independence

Author Maciej Skorski

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Maciej Skorski
  • University of Luxembourg, Luxembourg


The author thanks the reviewers of RANDOM'22 for insightful comments.

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Maciej Skorski. Tight Chernoff-Like Bounds Under Limited Independence. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 245, pp. 15:1-15:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


This paper develops sharp bounds on moments of sums of k-wise independent bounded random variables, under constrained average variance. The result closes the problem addressed in part in the previous works of Schmidt et al. and Bellare, Rompel. The work also discusses other applications of independent interests, such as asymptotically sharp bounds on binomial moments.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Statistical paradigms
  • Mathematics of computing → Probabilistic inference problems
  • Theory of computation → Randomness, geometry and discrete structures
  • concentration inequalities
  • tail bounds
  • limited independence
  • k-wise independence


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