Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

This paper surveys quantum learning theory: the theoretical aspects of machine learning
using quantum computers. We describe the main results known for three models of learning …

We present a machine learning algorithm for building classifiers that are comprised of a
small number of short rules. These are restricted disjunctive normal form models. An …

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 …

From the winner of the Turing Award and the Abel Prize, an introduction to computational
complexity theory, its connections and interactions with mathematics, and its central role in …

Consider the problem of evaluating an AND-OR formula on an N-bit black-box input. We
present a bounded-error quantum algorithm that solves this problem in time N^1/2+o(1). In …

We give the first polynomial time algorithm to learn any function of a constant number of
halfspaces under the uniform distribution on the Boolean hypercube to within any constant …

We address well-studied problems concerning the learn-ability of parities and halfspaces in
the presence of classification noise. Learning of parities under the uniform distribution with …

A Brief Introduction to Fourier Analysis on the Boolean Cube Page 1 Theory OF Computing
Library Graduate Surveys, TCGS 1 (2008), pp. 1–20 http://theoryofcomputing.org A Brief …

We design new polynomials for representing threshold functions in three different regimes:
probabilistic polynomials of low degree, which need far less randomness than previous …