DeepAI AI Chat

Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes

07/13/2016

∙

by Chengtao Li, et al.

∙

MIT

∙

∙

In this note we consider sampling from (non-homogeneous) strongly Rayleigh probability measures. As an important corollary, we obtain a fast mixing Markov Chain sampler for Determinantal Point Processes.

Chengtao Li
11 publications
Stefanie Jegelka
67 publications
Suvrit Sra
79 publications

page 1

page 2

page 3

page 4

∙ 08/02/2016

Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling

We study probability measures induced by set functions with constraints....

0 Chengtao Li, et al. ∙

∙ 06/06/2015

Fast Mixing for Discrete Point Processes

We investigate the systematic mechanism for designing fast mixing Markov...

0 Patrick Rebeschini, et al. ∙

∙ 03/14/2019

Modified log-Sobolev inequalities for strongly log-concave distributions

We show that the modified log-Sobolev constant for a natural Markov chai...

0 Mary Cryan, et al. ∙

∙ 09/19/2018

DPPy: Sampling Determinantal Point Processes with Python

Determinantal point processes (DPPs) are specific probability distributi...

0 Guillaume Gautier, et al. ∙

∙ 03/08/2017

Polynomial Time Algorithms for Dual Volume Sampling

We study dual volume sampling, a method for selecting k columns from an ...

0 Chengtao Li, et al. ∙

∙ 04/06/2022

Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence

We design fast algorithms for repeatedly sampling from strongly Rayleigh...

0 Nima Anari, et al. ∙

∙ 03/21/2019

Exact slice sampler for Hierarchical Dirichlet Processes

We propose an exact slice sampler for Hierarchical Dirichlet process (HD...

0 Arash A. Amini, et al. ∙