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Consider a Boolean function f on the n-dimensional hypercube, and a set of variables (indexed by) $S \subset \{1,2,\ldots,n\}.$ The coalition influence of the variables S on a function f is the probability that after a random assignment of…

Combinatorics · Mathematics 2026-01-19 Tomasz Przybyłowski

In this expository article, we study the relation between the boolean functions and the hypercontractivity theorems of Aline Bonami. We focus on the social choice theory, and present some of the most important results in the area, such as…

Classical Analysis and ODEs · Mathematics 2025-02-20 María José González , Paul MacManus , María Cristina Pereyra

About twenty years ago we wrote a paper, "Boolean Functions whose Fourier Transform is Concentrated on the First Two Levels", \cite{FKN}. In it we offered several proofs of the statement that Boolean functions $f(x_1,x_2,\dots,x_n)$, whose…

Combinatorics · Mathematics 2021-05-10 Ehud Friedgut , GIl Kalai , Assaf Naor

It was shown in a previous work that, for systems in which the entropy is an extensive function of the energy and volume, the Bekenstein and the holographic entropy bounds predict new results. In this paper, we go further and derive…

General Relativity and Quantum Cosmology · Physics 2009-11-10 Gilad Gour

The influence of a variable is an important concept in the analysis of Boolean functions. The more general notion of influence of a set of variables on a Boolean function has four separate definitions in the literature. In the present work,…

Information Theory · Computer Science 2023-02-15 Aniruddha Biswas , Palash Sarkar

The number of $n$-ary bent functions is less than $2^{3\cdot2^{n-3}(1+o(1))}$ as $n$ is even and $n\rightarrow\infty$. Keywords: Boolean function, bent function, upper bound

Information Theory · Computer Science 2023-03-30 Vladimir N. Potapov

In this paper we study functions with low influences on product probability spaces. The analysis of boolean functions with low influences has become a central problem in discrete Fourier analysis. It is motivated by fundamental questions…

Probability · Mathematics 2007-05-23 Elchanan Mossel , Ryan O'Donnell , Krzysztof Oleszkiewicz

A function $f:\ \{-1,1\}^n\rightarrow \mathbb{R}$ is called pseudo-Boolean. It is well-known that each pseudo-Boolean function $f$ can be written as $f(x)=\sum_{I\in {\cal F}}\hat{f}(I)\chi_I(x),$ where ${\cal F}\subseteq \{I:\ I\subseteq…

Discrete Mathematics · Computer Science 2012-12-04 Gregory Gutin , Anders Yeo

Let $f\colon \{-1,1\}^n \to [-1,1]$ have degree $d$ as a multilinear polynomial. It is well-known that the total influence of $f$ is at most $d$. Aaronson and Ambainis asked whether the total $L_1$ influence of $f$ can also be bounded as a…

Computational Complexity · Computer Science 2015-03-31 Yuval Filmus , Hamed Hatami , Nathan Keller , Noam Lifshitz

A Boolean function is called read-once over a basis B if it can be expressed by a formula over B where no variable appears more than once. A checking test for a read-once function f over B depending on all its variables is a set of input…

Discrete Mathematics · Computer Science 2012-05-29 Dmitry V. Chistikov

We prove the "Most informative boolean function" conjecture of Courtade and Kumar for high noise $\epsilon \ge 1/2 - \delta$, for some absolute constant $\delta > 0$. Namely, if $X$ is uniformly distributed in $\{0,1\}^n$ and $Y$ is…

Information Theory · Computer Science 2015-11-29 Alex Samorodnitsky

A subset $S$ of the Boolean hypercube $\mathbb{F}_2^n$ is a sumset if $S = \{a + b : a, b\in A\}$ for some $A \subseteq \mathbb{F}_2^n$. Sumsets are central objects of study in additive combinatorics, featuring in several influential…

Data Structures and Algorithms · Computer Science 2024-02-06 Xi Chen , Shivam Nadimpalli , Tim Randolph , Rocco A. Servedio , Or Zamir

Extending previous analyses on function classes like linear functions, we analyze how the simple (1+1) evolutionary algorithm optimizes pseudo-Boolean functions that are strictly monotone. Contrary to what one would expect, not all of these…

Neural and Evolutionary Computing · Computer Science 2015-03-17 Benjamin Doerr , Thomas Jansen , Dirk Sudholt , Carola Winzen , Christine Zarges

We study the computational power of polynomial threshold functions, that is, threshold functions of real polynomials over the boolean cube. We provide two new results bounding the computational power of this model. Our first result shows…

Computational Complexity · Computer Science 2009-11-29 Ido Ben-Eliezer , Shachar Lovett , Ariel Yadin

Let $ x\geq 1 $ be a large number, let $ [x]=x-\{x\} $ be the largest integer function, and let $ \varphi(n)$ be the Euler totient function. The result $ \sum_{n\leq x}\varphi([x/n])=(6/\pi^2)x\log x+O\left ( x(\log x)^{2/3}(\log\log…

General Mathematics · Mathematics 2021-04-12 N. A. Carella

Contemporary predictive models are hard to interpret as their deep nets exploit numerous complex relations between input elements. This work suggests a theoretical framework for model interpretability by measuring the contribution of…

Machine Learning · Computer Science 2022-06-15 Itai Gat , Nitay Calderon , Roi Reichart , Tamir Hazan

In this paper we derive tight bounds on the expected value of products of {\em low influence} functions defined on correlated probability spaces. The proofs are based on extending Fourier theory to an arbitrary number of correlated…

Probability · Mathematics 2009-06-01 Elchanan Mossel

Lattice induced threshold function is a Boolean function determined by a particular linear combination of lattice elements. We prove that every isotone Boolean function is a lattice induced threshold function and vice versa. We also…

Rings and Algebras · Mathematics 2013-07-05 Eszter K. Horváth , Branimir Seselja , Andreja Tepavcevic

This work is dedicated to the development of the theory of Fourier hyperfunctions in one variable with values in a complex non-necessarily metrisable locally convex Hausdorff space $E$. Moreover, necessary and sufficient conditions are…

Functional Analysis · Mathematics 2026-04-20 Karsten Kruse

We prove that for any decision tree calculating a boolean function $f:\{-1,1\}^n\to\{-1,1\}$, \[ \Var[f] \le \sum_{i=1}^n \delta_i \Inf_i(f), \] where $\delta_i$ is the probability that the $i$th input variable is read and $\Inf_i(f)$ is…

Computational Complexity · Computer Science 2007-05-23 Ryan O'Donnell , Michael Saks , Oded Schramm , Rocco A. Servedio