2024-04-18-concha-duarte24a.md

title	software	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization	https://github.com/AlejandrodelaConcha/OLRE	Quantifying the difference between two probability density functions, $p$ and $q$, using available data, is a fundamental problem in Statistics and Machine Learning. A usual approach for addressing this problem is the likelihood-ratio estimation (LRE) between $p$ and $q$, which -to our best knowledge- has been investigated mainly for the offline case. This paper contributes by introducing a new framework for online non-parametric LRE (OLRE) for the setting where pairs of iid observations $(x_t \sim p, x’_t \sim q)$ are observed over time. The non-parametric nature of our approach has the advantage of being agnostic to the forms of $p$ and $q$. Moreover, we capitalize on the recent advances in Kernel Methods and functional minimization to develop an estimator that can be efficiently updated at every iteration. We provide theoretical guarantees for the performance of the OLRE method along with empirical validation in synthetic experiments.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	concha-duarte24a	0	Online non-parametric likelihood-ratio estimation by {P}earson-divergence functional minimization	1189	1197	1189-1197	1189	false	de la Concha Duarte, Alejandro D. and Vayatis, Nicolas and Kalogeratos, Argyris	given family prefix Alejandro D. Concha Duarte de la given family Nicolas Vayatis given family Argyris Kalogeratos	2024-04-18		Proceedings of The 27th International Conference on Artificial Intelligence and Statistics	238	inproceedings	date-parts 2024 4 18	https://proceedings.mlr.press/v238/concha-duarte24a/concha-duarte24a.pdf