1 citations · 1 across the 3 of their papers we have counts for
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The Well-Tempered Classifier: Some Elementary Properties of Temperature Scaling
Pierre-Alexandre Mattei, Bruno Loureiro
Temperature scaling is a simple method that allows to control the uncertainty of probabilistic models. It is mostly used in two contexts: improving the calibration of classifiers a…
Beyond Mixtures and Products for Ensemble Aggregation: A Likelihood Perspective on Generalized Means
Raphaël Razafindralambo, Rémy Sun, Frédéric Precioso +2
Density aggregation is a central problem in machine learning, for instance when combining predictions from a Deep Ensemble. The choice of aggregation remains an open question with…
Are Ensembles Getting Better all the Time?
Pierre-Alexandre Mattei, Damien Garreau
Ensemble methods combine the predictions of several base models. We study whether or not including more models always improves their average performance. This question depends on t…
Parsimonious Gaussian mixture models with piecewise-constant eigenvalue profiles
Tom Szwagier, Pierre-Alexandre Mattei, Charles Bouveyron +1
Gaussian mixture models (GMMs) are ubiquitous in statistical learning, particularly for unsupervised problems. While full GMMs suffer from the overparameterization of their covaria…
A Tutorial on Discriminative Clustering and Mutual Information
Louis Ohl, Pierre-Alexandre Mattei, Frédéric Precioso
To cluster data is to separate samples into distinctive groups that should ideally have some cohesive properties. Today, numerous clustering algorithms exist, and their differences…
Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection
Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron +3
Feature selection in clustering is a hard task which involves simultaneously the discovery of relevant clusters as well as relevant variables with respect to these clusters. While…