We give an algorithm that learns any monotone Boolean function f : {-1, 1}n {-1, 1} to any constant accuracy, under the uniform distribution, in time polynomial in n and in the de...
In many real-life situations, we know the probability distribution of two random variables x1 and x2, but we have no information about the correlation between x1 and x2; what are ...
Abstract. In this paper, we propose a Markov chain for sampling a random vector distributed according to a discretized Dirichlet distribution. We show that our Markov chain is rapi...
Motivated by the capabilities of modern storage architectures, we consider the following generalization of the data stream model where the algorithm has sequential access to multi...
In this paper, we develop a new "robust mixing" framework for reasoning about adversarially modified Markov Chains (AMMC). Let P be the transition matrix of an irreducib...