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 show that completeness at higher levels of the theory of the reals is...
We present improved algorithms and matching statistical and computationa...
We study the computational complexity of estimating local observables fo...
For every n, we construct two curves in the plane that intersect at leas...
We study the performance of Markov chains for the q-state ferromagnetic
...
Spectral independence is a recently-developed framework for obtaining sh...
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...
The Swendsen-Wang algorithm is a sophisticated, widely-used Markov chain...
The spectral independence approach of Anari et al. (2020) utilized recen...
Deep learning architectures with a huge number of parameters are often
c...
We study identity testing for restricted Boltzmann machines (RBMs), and ...
We prove that, unless P=NP, there is no polynomial-time algorithm to
app...
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
...
We study the problem of approximating the value of the matching polynomi...
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 complexity of approximating the independent set polynomial
...
We study the structure learning problem for graph homomorphisms, commonl...
The Gibbs sampler is a particularly popular Markov chain used for learni...
The cardinality constraint is an intrinsic way to restrict the solution
...
Given a Gaussian Markov random field, we consider the problem of selecti...