1.4k citations · 1.9k across the 49 of their papers we have counts for
53 papers
A Unifying Framework for Global Optimization: From Theory to Formalization
Gaëtan Serré, Argyris Kalogeratos, Nicolas Vayatis
We introduce an abstract measure___theoretic framework that serves as a tool to rigorously study stochastic iterative global optimization algorithms as a unified class. The framewo…
LIPO+: Frugal Global Optimization for Lipschitz Functions
Gaëtan Serré, Perceval Beja-Battais, Sophia Chirrane +2
In this paper, we propose simple yet effective empirical improvements to the algorithms of the LIPO family, introduced in [Malherbe2017], that we call LIPO+ and AdaLIPO+. We compar…
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…
Stein Boltzmann Sampling: A Variational Approach for Global Optimization
Gaëtan Serré, Argyris Kalogeratos, Nicolas Vayatis
In this paper, we present a flow-based method for global optimization of continuous Sobolev functions, called Stein Boltzmann Sampling (SBS). SBS initializes uniformly a number of…
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…