37 citations · 120 across the 35 of their papers we have counts for
4 papers · 1 filter
Fast sampling and counting k-SAT solutions in the local lemma regime
Weiming Feng, Heng Guo, Yitong Yin +1
We give new algorithms based on Markov chains to sample and approximately count satisfying assignments to -uniform CNF formulas where each variable appears at most times. Fo…
Perfect sampling from spatial mixing
Weiming Feng, Heng Guo, Yitong Yin
We introduce a new perfect sampling technique that can be applied to general Gibbs distributions and runs in linear time if the correlation decays faster than the neighborhood grow…
Dynamic inference in probabilistic graphical models
Weiming Feng, Kun He, Xiaoming Sun +1
Probabilistic graphical models, such as Markov random fields (MRFs), are useful for describing high-dimensional distributions in terms of local dependence structures. The probabili…
Distributed Metropolis Sampler with Optimal Parallelism
Weiming Feng, Thomas P. Hayes, Yitong Yin
The Metropolis-Hastings algorithm is a fundamental Markov chain Monte Carlo (MCMC) method for sampling and inference. With the advent of Big Data, distributed and parallel variants…