@inproceedings{14f9c2919ac44ed1a1c8f3a309d341fa,

title = "Scalar and Matrix Chernoff Bounds from ℓ∞-Independence",

abstract = "We present new scalar and matrix Chernoff-style concentration bounds for a broad class of probability distributions over the binary hypercube {0, 1}n. Motivated by recent tools developed for the study of mixing times of Markov chains on discrete distributions, we say that a distribution is ℓ∞-independent when the infinity norm of its influence matrix is bounded by a constant. We show that any distribution which is ℓ∞-infinity independent satisfies a matrix Chernoff bound that matches the matrix Chernoff bound for independent random variables due to Tropp. Our matrix Chernoff bound is a broad generalization and strengthening of the matrix Chernoff bound of Kyng and Song (FOCS'18).",

author = "Tali Kaufman and Rasmus Kyng and Federico Sold{\'a}",

note = "Publisher Copyright: Copyright {\textcopyright} 2022 by SIAM.; 33rd Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2022 ; Conference date: 09-01-2022 Through 12-01-2022",

year = "2022",

language = "الإنجليزيّة",

series = "Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms",

pages = "3732--3753",

booktitle = "ACM-SIAM Symposium on Discrete Algorithms, SODA 2022",

}