Emergent Mind
A Polynomial Time MCMC Method for Sampling from Continuous DPPs
(1810.08867)
Published Oct 20, 2018
in
cs.LG
,
cs.DS
,
and
stat.ML
Abstract
We study the Gibbs sampling algorithm for continuous determinantal point processes. We show that, given a warm start, the Gibbs sampler generates a random sample from a continuous $k$-DPP defined on a $d$-dimensional domain by only taking $\text{poly}(k)$ number of steps. As an application, we design an algorithm to generate random samples from $k$-DPPs defined by a spherical Gaussian kernel on a unit sphere in $d$-dimensions, $\mathbb{S}{d-1}$ in time polynomial in $k,d$.
We're not able to analyze this paper right now due to high demand.
Please check back later (sorry!).
Generate a summary of this paper on our Pro plan:
We ran into a problem analyzing this paper.