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Related papers: Analysis of Boolean Functions

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A list of open problems in the field of analysis of boolean functions, compiled February 2012 for the Simons Symposium.

Discrete Mathematics · Computer Science 2012-05-01 Ryan O'Donnell

The subject of this textbook is the analysis of Boolean functions. Roughly speaking, this refers to studying Boolean functions $f : \{0,1\}^n \to \{0,1\}$ via their Fourier expansion and other analytic means. Boolean functions are perhaps…

Discrete Mathematics · Computer Science 2021-05-24 Ryan O'Donnell

We discuss ways in which tools from topology can be used to derive lower bounds for the circuit complexity of Boolean functions.

Combinatorics · Mathematics 2022-11-15 Anders Björner , Mark Goresky , Robert MacPherson

We compute the nonlinearity of Boolean functions with Groebner basis techniques, providing two algorithms: one over the binary field and the other over the rationals. We also estimate their complexity. Then we show how to improve our…

Information Theory · Computer Science 2014-04-11 E. Bellini , I. Simonetti , M. Sala

We associate to each Boolean function a polynomial whose evaluations represents the distances from all possible Boolean affine functions. Both determining the coefficients of this polynomial from the truth table of the Boolean function and…

Information Theory · Computer Science 2014-04-11 Emanuele Bellini

This document collects the lecture notes from my mini-course "Complexity Theory, Game Theory, and Economics," taught at the Bellairs Research Institute of McGill University, Holetown, Barbados, February 19--23, 2017, as the 29th McGill…

Computational Complexity · Computer Science 2020-02-11 Tim Roughgarden

We present new algorithms to compute fundamental properties of a Boolean function given in truth-table form. Specifically, we give an O(N^2.322 log N) algorithm for block sensitivity, an O(N^1.585 log N) algorithm for `tree decomposition,'…

Computational Complexity · Computer Science 2007-05-23 Scott Aaronson

This paper presents complexity analysis and variational methods for inference in probabilistic description logics featuring Boolean operators, quantification, qualified number restrictions, nominals, inverse roles and role hierarchies.…

Artificial Intelligence · Computer Science 2012-05-14 Fabio Gagliardi Cozman , Rodrigo Bellizia Polastro

We show (almost) separation between certain important classes of Boolean functions. The technique that we use is to show that the total influence of functions in one class is less than the total influence of functions in the other class. In…

Computational Complexity · Computer Science 2020-10-23 Aniruddha Biswas , Palash Sarkar

Abduction is a fundamental and important form of non-monotonic reasoning. Given a knowledge base explaining how the world behaves it aims at finding an explanation for some observed manifestation. In this paper we focus on propositional…

Computational Complexity · Computer Science 2010-06-29 Nadia Creignou , Johannes Schmidt , Michael Thomas

Deep learning is computationally intensive, with significant efforts focused on reducing arithmetic complexity, particularly regarding energy consumption dominated by data movement. While existing literature emphasizes inference, training…

Machine Learning · Statistics 2025-06-09 Van Minh Nguyen , Cristian Ocampo , Aymen Askri , Louis Leconte , Ba-Hien Tran

This paper describes a purely functional library for computing level-$p$-complexity of Boolean functions, and applies it to two-level iterated majority. Boolean functions are simply functions from $n$ bits to one bit, and they can describe…

Programming Languages · Computer Science 2023-12-13 Julia Jansson , Patrik Jansson

Classification of Non-linear Boolean functions is a long-standing problem in the area of theoretical computer science. In this paper, effort has been made to achieve a systematic classification of all n-variable Boolean functions, where…

Logic in Computer Science · Computer Science 2013-03-15 Ranjeet Kumar Rout , Pabitra Pal Choudhury , Sudhakar Sahoo

In this work we cast the problem of binary classification in terms of estimating a partition on Bernoulli data. When the explanatory variables are all categorical, the problem can be modelled using the language of boolean functions. We…

Machine Learning · Statistics 2020-03-24 Paulo Hubert

Apart from an account of classical preliminaries, this volume contains a systematic introduction to Sobolev spaces and functions of bounded variation with selected applications. This is installment III of a four part discussion of certain…

History and Overview · Mathematics 2024-02-05 Garth Warner

Inspired by Solomonoffs theory of inductive inference, we propose a prior based on circuit complexity. There are several advantages to this approach. First, it relies on a complexity measure that does not depend on the choice of UTM. There…

Machine Learning · Computer Science 2023-06-27 Cole Wyeth , Carl Sturtivant

This is (raw) lecture notes of the course read on 6th European intensive course on Complex Analysis (Coimbra, Portugal) in 2000. Our purpose is to describe a general framework for generalizations of the complex analysis. As a consequence a…

Complex Variables · Mathematics 2007-05-23 Vladimir V. Kisil

A number of complexity measures for Boolean functions have previously been introduced. These include (1) sensitivity, (2) block sensitivity, (3) witness complexity, (4) subcube partition complexity and (5) algorithmic complexity. Each of…

Probability · Mathematics 2024-08-26 Laurin Köhler-Schindler , Jeffrey E. Steif

We consider various definitions of degrees of discrete functions and establish relations between the number of relevant (essential) variables and degrees of two- and three-valued functions. Keywords: relevant variable, sensitivity, degree…

Combinatorics · Mathematics 2025-12-01 V. N. Potapov

The goal of the paper is to relate complexity measures associated with the evaluation of Boolean functions (certificate complexity, decision tree complexity) and learning dimensions used to characterize exact learning (teaching dimension,…

Machine Learning · Computer Science 2012-05-22 Sergiu Goschin
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