We consider the problem of learning sparse parities in the presence of noise. For learning parities on r out of n variables, we give an algorithm that runs in time poly log 1 δ , ...
We say that a distribution over {0, 1}n is ( , k)-wise independent if its restriction to every k coordinates results in a distribution that is -close to the uniform distribution. ...
The objective of this paper is twofold. First, the problem of generation of real random matrix samples with uniform distribution in structured (spectral) norm bounded sets is stud...
In this paper, we propose the definition of a measure for sets of strings of length not greater than a given number. This measure leads to an instanciation of the uniform distribut...
In this paper, we describe a new algorithm for sampling solutions from a uniform distribution over the solutions of a constraint network. Our new algorithm improves upon the Sampli...
In this paper we present a randomized parallel algorithm to sample matchings from an almost uniform distribution on the set of matchings of all sizes in a graph. First we prove th...
Randomness extractors convert weak sources of randomness into an almost uniform distribution; the conversion uses a small amount of pure randomness. In algorithmic applications, t...
We show that the class of monotone 2O( √ log n)-term DNF formulae can be PAC learned in polynomial time under the uniform distribution from random examples only. This is an expo...
A true random number generator (TRNG) usually consists of two components: an “unpredictable” source with high entropy, and a randomness extractor — a function which, when app...
We address well-studied problems concerning the learnability of parities and halfspaces in the presence of classification noise. Learning of parities under the uniform distributi...