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Related papers: Sampling and entropy numbers in the uniform norm

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Sharp, nonasymptotic bounds are obtained for the relative entropy between the distributions of sampling with and without replacement from an urn with balls of $c\geq 2$ colors. Our bounds are asymptotically tight in certain regimes and,…

Probability · Mathematics 2026-01-14 Oliver Johnson , Lampros Gavalakis , Ioannis Kontoyiannis

We prove bounds for the covering numbers of classes of convex functions and convex sets in Euclidean space. Previous results require the underlying convex functions or sets to be uniformly bounded. We relax this assumption and replace it…

Information Theory · Computer Science 2014-10-24 Adityanand Guntuboyina

We provide a sharp lower bound on the $p$-norm of a sum of independent uniform random variables in terms of its variance when $0 < p < 1$. We address an analogous question for $p$-R\'enyi entropy for $p$ in the same range.

Probability · Mathematics 2025-01-28 Giorgos Chasapis , Keerthana Gurushankar , Tomasz Tkocz

Discretization of the uniform norm of functions from a given finite dimensional subspace of continuous functions is studied. Previous known results show that for any $N$-dimensional subspace of the space of continuous functions it is…

Functional Analysis · Mathematics 2024-05-08 E. D. Kosov , V. N. Temlyakov

We find a sharp combinatorial bound for the metric entropy of sets in R^n and general classes of functions. This solves two basic combinatorial conjectures on the empirical processes. 1. A class of functions satisfies the uniform Central…

Functional Analysis · Mathematics 2016-12-23 Mark Rudelson , Roman Vershynin

We consider infinitely dimensional classes of functions and instead of the relative error setting, which was used in previous papers on the integral norm discretization, we consider the absolute error setting. We demonstrate how known…

Numerical Analysis · Mathematics 2022-03-15 V. N. Temlyakov

We derive an upper bound for the mean of the supremum of the empirical process indexed by a class of functions that are known to have variance bounded by a small constant $\delta$. The bound is expressed in the uniform entropy integral of…

Statistics Theory · Mathematics 2010-12-30 Aad van der Vaart , Jon A. Wellner

In this paper, we develop a general machinery for finding explicit uniform probability and moment bounds on sub-additive positive functionals of random processes. Using the developed general technique, we derive uniform bounds on the…

Probability · Mathematics 2012-02-09 Alexander Goldenshluger , Oleg Lepski

This paper studies the behavior of the entropy numbers of classes of functions with bounded integral norms from a given finite dimensional linear subspace. Upper bounds of these entropy numbers in the uniform norm are obtained and applied…

Classical Analysis and ODEs · Mathematics 2020-01-30 F. Dai , A. Prymak , A. Shadrin , V. Temlyakov , S. Tikhonov

We show how to determine the maximum and minimum possible values of one measure of entropy for a given value of another measure of entropy. These maximum and minimum values are obtained for two standard forms of probability distribution (or…

Quantum Physics · Physics 2007-05-23 Dominic W. Berry , Barry C. Sanders

We study an entropy measure for quantum systems that generalizes the von Neumann entropy as well as its classical counterpart, the Gibbs or Shannon entropy. The entropy measure is based on hypothesis testing and has an elegant formulation…

Quantum Physics · Physics 2014-02-19 F. Dupuis , L. Kraemer , P. Faist , J. M. Renes , R. Renner

Shannon entropy is widely used to quantify the uncertainty of discrete random variables. But when normalized to the unit interval, as is often done in practice, it no longer conveys the alphabet sizes of the random variables being studied.…

Information Theory · Computer Science 2022-07-26 John Çamkıran

Uniform convergence rates are provided for asymptotic representations of sample extremes. These bounds which are universal in the sense that they do not depend on the extreme value index are meant to be extended to arbitrary samples…

We derive a measurement-independent asymptotic continuity bound on the observational entropy for general POVM measurements, making essential use of its property of bounded concavity. The same insight is used to obtain continuity bounds for…

Quantum Physics · Physics 2024-09-25 Joseph Schindler , Andreas Winter

In this paper we study the covering numbers of the space of convex and uniformly bounded functions in multi-dimension. We find optimal upper and lower bounds for the $\epsilon$-covering number of $\C([a, b]^d, B)$, in the $L_p$-metric, $1…

Information Theory · Computer Science 2012-04-03 Adityanand Guntuboyina , Bodhisattva Sen

In the present paper we prove a family of tight upper and lower bounds for the Shannon entropy and von Neumann entropy based on the p-norms. This allows us to have an entropy estimate, a criterion for the finiteness of it and a bound on the…

Information Theory · Computer Science 2024-08-22 Juan Pablo Lopez

We present a positive solution to the so-called Bernoulli Conjecture concerning the characterization of sample boundedness of Bernoulli processes. We also discuss some applications and related open problems.

Probability · Mathematics 2014-09-19 Witold Bednorz , Rafał Latała

For convex univalent functions we give instances where the sharp bound for various coefficient functionals are identical to those for the corresponding bound for the inverse function. We give instances where the sharp bounds differ and also…

Complex Variables · Mathematics 2022-12-12 Derek K. Thomas

The paper addresses the problem of sampling discretization of integral norms of elements of finite-dimensional subspaces satisfying some conditions. We prove sampling discretization results under two standard kinds of assumptions --…

Numerical Analysis · Mathematics 2021-09-21 F. Dai , V. Temlyakov

We give new upper and lower bounds on the concavity of quantum entropy. Comparisons are given with other results in the literature.

Quantum Physics · Physics 2015-06-19 Isaac H. Kim , Mary Beth Ruskai
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