4 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…
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…
Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization
Antoine de Mathelin, François Deheeger, Mathilde Mougeot +1
This paper deals with uncertainty quantification and out-of-distribution detection in deep learning using Bayesian and ensemble methods. It proposes a practical solution to the lac…
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…