Strong spatial mixing (SSM) is an important quantitative notion of correlation decay for Gibbs
distributions arising in statistical physics, probability theory, and theoretical computer …

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 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 …

Let Φ be a random k-CNF formula on n variables and m clauses, where each clause is a
disjunction of k literals chosen independently and uniformly. Our goal is, for most Φ, to …

In this work, we study how to efficiently obtain perfect samples from a discrete distribution
$\mathcal {D} $ given access only to pairwise comparisons of elements of its support …

We give a simple combinatorial algorithm to deterministically approximately count the
number of satisfying assignments of general constraint satisfaction problems (CSPs) …

We give an efficient perfect sampling algorithm for weighted, connected induced subgraphs
(or graphlets) of rooted, bounded degree graphs. Our algorithm utilizes a vertex-percolation …

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

We provide a perfect sampling algorithm for the hard-sphere model on subsets of $\mathbb
{R}^ d $ with expected running time linear in the volume under the assumption of strong …

Abstract Partial Rejection Sampling is an algorithmic approach to obtaining a perfect sample
from a specified distribution. The objects to be sampled are assumed to be represented by a …