1.4k citations · 1.9k across the 49 of their papers we have counts for
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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…
A framework for paired-sample hypothesis testing for high-dimensional data
Ioannis Bargiotas, Argyris Kalogeratos, Nicolas Vayatis
The standard paired-sample testing approach in the multidimensional setting applies multiple univariate tests on the individual features, followed by p-value adjustments. Such an a…
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…
Online non-parametric change-point detection for heterogeneous data streams observed over graph nodes
Alejandro de la Concha, Argyris Kalogeratos, Nicolas Vayatis
Consider a heterogeneous data stream being generated by the nodes of a graph. The data stream is in essence composed by multiple streams, possibly of different nature that depends…