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The approximate degree of a Boolean function $f(x_{1},x_{2},\ldots,x_{n})$ is the minimum degree of a real polynomial that approximates $f$ pointwise within $1/3$. Upper bounds on approximate degree have a variety of applications in…

Computational Complexity · Computer Science 2018-01-16 Alexander A. Sherstov

We introduce a numerical method for the approximation of functions which are analytic on compact intervals, except at the endpoints. This method is based on variable transforms using particular parametrized exponential and…

Numerical Analysis · Mathematics 2016-09-06 Ben Adcock , Jésus Martín-Vaquero , Mark Richardson

Let $X_1,\dots,X_n$ be independent nonnegative random variables (r.v.'s), with $S_n:=X_1+\dots+X_n$ and finite values of $s_i:=E X_i^2$ and $m_i:=E X_i>0$. Exact upper bounds on $E f(S_n)$ for all functions $f$ in a certain class…

Probability · Mathematics 2017-01-17 Iosif Pinelis

In this paper we offer a complete methodology for sufficient dimension reduction called the test function (TF). TF provides a new family of methods for the estimation of the central subspace (CS) based on the introduction of a nonlinear…

Statistics Theory · Mathematics 2011-06-08 François Portier , Bernard Delyon

In this paper, we study the problem of learning a monotone DNF with at most $s$ terms of size (number of variables in each term) at most $r$ ($s$ term $r$-MDNF) from membership queries. This problem is equivalent to the problem of learning…

Machine Learning · Computer Science 2014-05-06 Hasan Abasi , Nader H. Bshouty , Hanna Mazzawi

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 obtain the best approximation in $L^1(\R)$, by entire functions of exponential type, for a class of even functions that includes $e^{-\lambda|x|}$, where $\lambda >0$, $\log |x|$ and $|x|^{\alpha}$, where $-1 < \alpha < 1$. We also give…

Classical Analysis and ODEs · Mathematics 2011-06-06 Emanuel Carneiro , Jeffrey D. Vaaler

The theorem states that: Every Boolean function can be $\epsilon -approximated$ by a Disjunctive Normal Form (DNF) of size $O_{\epsilon}(2^{n}/\log{n})$. This paper will demonstrate this theorem in detail by showing how this theorem is…

Computational Complexity · Computer Science 2020-05-13 Yunhao Yang , Andrew Tan

Items in a test are often used as a basis for making decisions and such tests are therefore required to have good psychometric properties, like unidimensionality. In many cases the sum score is used in combination with a threshold to decide…

Methodology · Statistics 2020-07-14 Lourens Waldorp , Maarten Marsman , Denny Borsboom

For a Boolean function $\Phi\colon\{0,1\}^d\to\{0,1\}$ and an assignment to its variables $\mathbf{x}=(x_1, x_2, \dots, x_d)$ we consider the problem of finding the subsets of the variables that are sufficient to determine the function…

Computational Complexity · Computer Science 2019-06-19 Stephan Wäldchen , Jan Macdonald , Sascha Hauch , Gitta Kutyniok

A rational approximation by a ratio of polynomial functions is a flexible alternative to polynomial approximation. In particular, rational functions exhibit accurate estimations to nonsmooth and non- Lipschitz functions, where polynomial…

Optimization and Control · Mathematics 2020-02-27 V. Peiris , N. Sharon , N. Sukhorukova J. Ugon

We aim at providing a foundation of a theory of "good" SAT representations F of boolean functions f. We argue that the hierarchy UC_k of unit-refutation complete clause-sets of level k, introduced by the authors, provides the most basic…

Artificial Intelligence · Computer Science 2013-05-13 Matthew Gwynne , Oliver Kullmann

The Fourier-Walsh expansion of a Boolean function $f \colon \{0,1\}^n \rightarrow \{0,1\}$ is its unique representation as a multilinear polynomial. The Kindler-Safra theorem (2002) asserts that if in the expansion of $f$, the total weight…

Combinatorics · Mathematics 2019-01-28 Nathan Keller , Ohad Klein

Functional data analysis is a fast evolving branch of statistics. Estimation procedures for the popular functional linear model either suffer from lack of robustness or are computationally burdensome. To address these shortcomings, a…

Methodology · Statistics 2021-08-27 Ioannis Kalogridis , Stefan Van Aelst

It is known that there are classes of 2-CNFs requiring exponential size non-deterministic read-once branching programs to compute them. However, to the best of our knowledge, there are no superpolynomial lower bounds for branching programs…

Computational Complexity · Computer Science 2016-05-17 Igor Razgon

The purpose of this paper is to present the extended definitions and characterizations of the classical notions of APN and maximum nonlinear Boolean functions to deal with the case of mappings from a finite group K to another one N with the…

Cryptography and Security · Computer Science 2011-09-26 Laurent Poinsot , Alexander Pott

This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification. An integer…

Artificial Intelligence · Computer Science 2020-08-07 Sanjeeb Dash , Oktay Günlük , Dennis Wei

We note with B2 the Boole algebra with two elements. We define for the R->B2 functions the limits, the derivatives, the differentiability, the test functions, the integrals. We also define the distributions over the space of these test…

General Mathematics · Mathematics 2007-05-23 Serban E. Vlad

When data analysts train a classifier and check if its accuracy is significantly different from chance, they are implicitly performing a two-sample test. We investigate the statistical properties of this flexible approach in the…

Machine Learning · Computer Science 2020-02-18 Ilmun Kim , Aaditya Ramdas , Aarti Singh , Larry Wasserman

We design a new, fast algorithm for agnostically learning univariate probability distributions whose densities are well approximated by piecewise polynomial functions. Let $f$ be the density function of an arbitrary univariate distribution,…

Data Structures and Algorithms · Computer Science 2015-06-03 Jayadev Acharya , Ilias Diakonikolas , Jerry Li , Ludwig Schmidt