5 papers
Collaborative non-parametric two-sample testing
Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
This paper addresses the multiple two-sample test problem in a graph-structured setting, which is a common scenario in fields such as Spatial Statistics and Neuroscience. Each node…
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization
Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
Quantifying the difference between two probability density functions, and , using available data, is a fundamental problem in Statistics and Machine Learning. A usual approa…
Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation
Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis
Consider each node of a graph to be generating a data stream that is synchronized and observed at near real-time. At a change-point , a change occurs at a subset of nodes , w…
Collaborative likelihood-ratio estimation over graphs
Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
Assuming we have iid observations from two unknown probability density functions (pdfs), and , the likelihood-ratio estimation (LRE) is an elegant approach to compare the tw…
Offline detection of change-points in the mean for stationary graph signals
Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos
This paper addresses the problem of segmenting a stream of graph signals: we aim to detect changes in the mean of a multivariate signal defined over the nodes of a known graph. We…