AISTATS 2022poster13 citations
Sampling from Arbitrary Functions via PSD Models
Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi
Abstract
In many areas of applied statistics and machine learning, generating an arbitrary number of inde- pendent and identically distributed (i.i.d.) samples from a given distribution is a key task. When the distribution is known only through evaluations of the density, current methods either scale badly with the dimension or require very involved implemen- tations. Instead, we take a two-step approach by first modeling the probability distribution and then sampling from that model. We use the recently introduced class of positive semi-definite (PSD) models which have been shown to be e
BibTeX
@InProceedings{pmlr-v151-marteau-ferey22a,
title = { Sampling from Arbitrary Functions via PSD Models },
author = {Marteau-Ferey, Ulysse and Bach, Francis and Rudi, Alessandro},
booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
pages = {2823--2861},
year = {2022},
editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel},
volume = {151},
series = {Proceedings of Machine Learning Research},
month = {28--30 Mar},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v151/marteau-ferey22a/marteau-ferey22a.pdf},
url = {https://proceedings.mlr.press/v151/marteau-ferey22a.html},
abstract = { In many areas of applied statistics and machine learning, generating an arbitrary number of inde- pendent and identically distributed (i.i.d.) samples from a given distribution is a key task. When the distribution is known only through evaluations of the density, current methods either scale badly with the dimension or require very involved implemen- tations. Instead, we take a two-step approach by first modeling the probability distribution and then sampling from that model. We use the recently introduced class of positive semi-definite (PSD) models which have been shown to be e }
}