Markov Chain Monte Carlo (MCMC) algorithms are a widely-used algorithmic...
We present the first polynomial-time algorithm to exactly compute the nu...
These are self-contained lecture notes for spectral independence. For an...
We study the mixing time of the single-site update Markov chain, known a...
We present improved algorithms and matching statistical and computationa...
We study the computational complexity of estimating local observables fo...
We study the performance of Markov chains for the q-state ferromagnetic
...
This paper formalizes connections between stability of polynomials and
c...
For spin systems, such as the q-colorings and independent-set models,
ap...
For general spin systems, we prove that a contractive coupling for any l...
We prove an optimal mixing time bound on the single-site update Markov c...
The Swendsen-Wang algorithm is a sophisticated, widely-used Markov chain...
The spectral independence approach of Anari et al. (2020) utilized recen...
We study identity testing for restricted Boltzmann machines (RBMs), and ...
We prove that, unless P=NP, there is no polynomial-time algorithm to
app...
For general antiferromagnetic 2-spin systems, including the hardcore mod...
Strong spatial mixing (SSM) is a form of correlation decay that has play...
We study the identity testing problem in the context of spin systems or
...
Jenssen, Keevash and Perkins give an FPTAS and an efficient sampling
alg...
The Swendsen-Wang dynamics is a popular algorithm for sampling from the ...
We consider the problem of sampling from the Potts model on random regul...
Counting perfect matchings has played a central role in the theory of
co...
We study the structure learning problem for graph homomorphisms, commonl...
The Gibbs sampler is a particularly popular Markov chain used for learni...