We give a fast algorithm for sampling uniform solutions of general constraint satisfaction
problems (CSPs) in a local lemma regime. Ihe expected running time of our algorithm is …

We give new algorithms based on Markov chains to sample and approximately count
satisfying assignments to k-uniform CNF formulas where each variable appears at most d …

We present a new framework to derandomise certain Markov chain Monte Carlo (MCMC)
algorithms. As in MCMC, we first reduce counting problems to sampling from a sequence of …

Given a graph G, a random (k, n)-list assignment L for edges of G is an assignment of an
independent, uniformly random set of colors to each edge e and a proper L-list coloring of G …

We give a nearly linear-time algorithm to approximately sample satisfying assignments in
the random $ k $-SAT model when the density of the formula scales exponentially with $ k …

We give a Markov chain based perfect sampler for uniform sampling solutions of constraint
satisfaction problems (CSP). Under some mild Lov\'asz local lemma conditions where each …

A classic and fundamental result, known as the Lovász Local Lemma, is a gem in the
probabilistic method of combinatorics. At a high level, its core message can be described by …

We consider the problem of estimating the parameters of a Markov Random Field with hard-
constraints using a single sample. As our main running examples, we use the k-SAT and the …

We study the problem of sampling an approximately uniformly random satisfying assignment
for atomic constraint satisfaction problems ie where each constraint is violated by only one …

We study the locations of complex zeroes of independence polynomials of bounded-degree
hypergraphs. For graphs, this is a long-studied subject with applications to statistical …