13 citations · 25 across the 2 of their papers we have counts for
5 papers
On two ways to use determinantal point processes for Monte Carlo integration
Guillaume Gautier, Rémi Bardenet, Michal Valko
The standard Monte Carlo estimator of relies on independent samples from and has variance of order . Replacing the samples with a…
On estimating the structure factor of a point process, with applications to hyperuniformity
Diala Hawat, Guillaume Gautier, Rémi Bardenet +1
Hyperuniformity is the study of stationary point processes with a sub-Poisson variance in a large window. In other words, counting the points of a hyperuniform point process that f…
Fast sampling from -ensembles
Guillaume Gautier, Rémi Bardenet, Michal Valko
We study sampling algorithms for -ensembles with time complexity less than cubic in the cardinality of the ensemble. Following Dumitriu & Edelman (2002), we see the ensemble as…
DPPy: Sampling DPPs with Python
Guillaume Gautier, Guillermo Polito, Rémi Bardenet +1
Determinantal point processes (DPPs) are specific probability distributions over clouds of points that are used as models and computational tools across physics, probability, stati…
Zonotope hit-and-run for efficient sampling from projection DPPs
Guillaume Gautier, Rémi Bardenet, Michal Valko
Determinantal point processes (DPPs) are distributions over sets of items that model diversity using kernels. Their applications in machine learning include summary extraction and…