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Related papers: Resilience for the Littlewood-Offord Problem

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Let $X,X_1,\ldots,X_n$ be independent identically distributed random variables. In this paper we study the behavior of concentration functions of weighted sums $\sum_{k=1}^{n} X_k a_k$ with respect to the arithmetic structure of…

Probability · Mathematics 2022-08-04 Yulia S. Eliseeva , Friedrich Götze , Andrei Yu. Zaitsev

Let $\xi$ be a real random variable with mean zero and variance one and $A={a_1,...,a_n}$ be a multi-set in $\R^d$. The random sum $$S_A := a_1 \xi_1 + ... + a_n \xi_n $$ where $\xi_i$ are iid copies of $\xi$ is of fundamental importance in…

Combinatorics · Mathematics 2013-01-03 Hoi H. Nguyen , Van H. Vu

Let $X,X_1,\ldots,X_n$ be independent identically distributed random variables. In this paper we study the behavior of concentration functions of weighted sums $\sum_{k=1}^{n}a_k X_k$ with respect to the arithmetic structure of coefficients…

Probability · Mathematics 2021-12-03 Yulia S. Eliseeva , Friedrich Götze , Andrei Yu. Zaitsev

Let eta_i be iid Bernoulli random variables, taking values -1,1 with probability 1/2. Given a multiset V of n integers v_1,..., v_n, we define the concentration probability as rho(V) := sup_{x} Pr(v_1 eta_1+...+ v_n eta_n=x). A classical…

Combinatorics · Mathematics 2011-01-18 Hoi Nguyen , Van Vu

In this paper, we consider the Littlewood-Offord problems in one dimension for the Curie-Weiss models. Let \[Q_n^{+}:=\sup_{x\in\mathbb{R}}\sup_{v_1,v_2,\ldots,v_n\geq 1}P(\sum_{i=1}^{n}v_i\varepsilon_i\in(x-1,x+1)),\]…

Probability · Mathematics 2024-11-18 Yinshan Chang , Xue Peng

Let $X,X_1,\ldots,X_n$ be independent identically distributed random variables. In this paper we study the behavior of concentration functions of weighted sums $\sum_{k=1}^{n}X_ka_k $ with respect to the arithmetic structure of coefficients…

Probability · Mathematics 2018-05-22 Friedrich Götze , Andrei Yu. Zaitsev

Consider a quadratic polynomial $f\left(\xi_{1},\dots,\xi_{n}\right)$ of independent Bernoulli random variables. What can be said about the concentration of $f$ on any single value? This generalises the classical Littlewood--Offord problem,…

Combinatorics · Mathematics 2020-08-11 Matthew Kwan , Lisa Sauermann

This paper considers a variation of the full-information secretary problem where the random variables to be observed are independent but not necessary identically distributed. The main result is a sharp lower bound for the optimal win…

Probability · Mathematics 2018-12-12 Pieter C. Allaart , Jose A. Islas

The paper deals with studying a connection of the Littlewood--Offord problem with estimating the concentration functions of some symmetric infinitely divisible distributions.

Probability · Mathematics 2022-11-15 Friedrich Götze , Andrei Yu. Zaitsev

The celebrated Littlewood-Offord problem asks for an upper bound on the probability that the random variable $\epsilon_1 v_1 + \cdots + \epsilon_n v_n$ lies in the Euclidean unit ball, where $\epsilon_1, \ldots, \epsilon_n \in \{-1, 1\}$…

Combinatorics · Mathematics 2019-05-01 Shravas Rao

We generalize and solve the $\roman{mod}\,q$ analogue of a problem of Littlewood and Offord, raised by Vaughan and Wooley, concerning the distribution of the $2^n$ sums of the form $\sum_{i=1}^n\varepsilon_ia_i$, where each $\varepsilon_i$…

Number Theory · Mathematics 2016-09-06 Jerrold R. Griggs

We prove anti-concentration results for polynomials of independent random variables with arbitrary degree. Our results extend the classical Littlewood-Offord result for linear polynomials, and improve several earlier estimates. We discuss…

Probability · Mathematics 2015-08-11 Raghu Meka , Oanh Nguyen , Van Vu

This paper provides a connection between the concentration of a random variable and the distribution of the roots of its probability generating function. Let $X$ be a random variable taking values in $\{0,\ldots,n\}$ with $\mathbb{P}(X =…

Probability · Mathematics 2022-10-10 Marcus Michelen , Julian Sahasrabudhe

For a random variable $X$ define $Q(X) = \sup_{x \in \mathbb{R}} \mathbb{P}(X=x)$. Let $X_1, \dots, X_n$ be independent integer random variables. Suppose $Q(X_i) \le \alpha_i \in (0,1]$ for each $i \in \{1, \dots, n\}$. Ju\v{s}kevi\v{c}ius…

Probability · Mathematics 2026-03-12 Valentas Kurauskas

The paper deals with studying a connection of the Littlewood--Offord problem with estimating the concentration functions of some symmetric infinitely divisible distributions. Some multivariate generalizations of results of Arak (1980) are…

Probability · Mathematics 2016-05-03 Yulia S. Eliseeva , Andrei Yu. Zaitsev

Let $\epsilon_{1},\ldots,\epsilon_{n}$ be a sequence of independent Rademacher random variables. We prove that there is a constant $c>0$ such that for any unit vectors $v_1,\ldots,v_n\in \mathbb{R}^2$, $$\Pr\left[||\epsilon_1…

Probability · Mathematics 2024-12-31 Xiaoyu He , Tomas Juskevicius , Bhargav Narayanan , Sam Spiro

We study the approximability of Max Ones when the number of variable occurrences is bounded by a constant. For conservative constraint languages (i.e., when the unary relations are included) we give a complete classification when the number…

Computational Complexity · Computer Science 2007-05-23 Fredrik Kuivinen

Let $P_{n}(x)= \sum_{i=0}^n \xi_i x^i$ be a Kac random polynomial where the coefficients $\xi_i$ are iid copies of a given random variable $\xi$. Our main result is an optimal quantitative bound concerning real roots repulsion. This leads…

Probability · Mathematics 2017-05-17 Yen Do , Hoi Nguyen , Van Vu

Classical anti-concentration results focus on the random sum $S := \sum _{i=1}^n \xi_i v_i$, where $\xi_i$ are independent random variables and $v_i$ are real numbers. In this paper, we prove new concentration results concerning the random…

Probability · Mathematics 2026-01-09 Aaron Berger , Ross Berkowitz , Pat Devlin , Van Vu

Learning theory has largely focused on two main learning scenarios. The first is the classical statistical setting where instances are drawn i.i.d. from a fixed distribution and the second scenario is the online learning, completely…

Machine Learning · Statistics 2011-04-28 Alexander Rakhlin , Karthik Sridharan , Ambuj Tewari