ICML 2019oral15 citations
Boosted Density Estimation Remastered
Abstract
There has recently been a steady increase in the number iterative approaches to density estimation. However, an accompanying burst of formal convergence guarantees has not followed; all results pay the price of heavy assumptions which are often unrealistic or hard to check. The
BibTeX
@InProceedings{pmlr-v97-cranko19b,
title = {Boosted Density Estimation Remastered},
author = {Cranko, Zac and Nock, Richard},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {1416--1425},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/cranko19b/cranko19b.pdf},
url = {https://proceedings.mlr.press/v97/cranko19b.html},
abstract = {There has recently been a steady increase in the number iterative approaches to density estimation. However, an accompanying burst of formal convergence guarantees has not followed; all results pay the price of heavy assumptions which are often unrealistic or hard to check. The