103 citations · 201 across the 29 of their papers we have counts for
31 papers
Normative Alignment of Recommender Systems via Internal Label Shift
Johannes Kruse, Kasper Lindskow, Michael Riis Andersen +4
We introduce NAILS (Normative Alignment of Recommender Systems via Internal Label Shift), a simple and scalable method for aligning recommendation outputs with target distributions…
Towards More General Control of Diffusion Models Using Jeffrey Guidance
Raphaël Razafindralambo, Rémy Sun, Frédéric Precioso +2
A key strength of diffusion models lies in their flexibility, since their outputs can be controlled at sampling time through guidance. However, beyond simple cases such as conditio…
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…
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…
When Are Two Scores Better Than One? Investigating Ensembles of Diffusion Models
Raphaël Razafindralambo, Rémy Sun, Frédéric Precioso +2
Diffusion models now generate high-quality, diverse samples, with an increasing focus on more powerful models. Although ensembling is a well-known way to improve supervised models,…
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…
Learning Energy-Based Models by Self-normalising the Likelihood
Hugo Senetaire, Paul Jeha, Pierre-Alexandre Mattei +1
Training an energy-based model (EBM) with maximum likelihood is challenging due to the intractable normalisation constant. Traditional methods rely on expensive Markov chain Monte…
Kernel KMeans clustering splits for end-to-end unsupervised decision trees
Louis Ohl, Pierre-Alexandre Mattei, Mickaël Leclercq +2
Trees are convenient models for obtaining explainable predictions on relatively small datasets. Although there are many proposals for the end-to-end construction of such trees in s…
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…
Generalised Mutual Information: a Framework for Discriminative Clustering
Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron +4
In the last decade, recent successes in deep clustering majorly involved the Mutual Information (MI) as an unsupervised objective for training neural networks with increasing regul…
Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models
Irene Balelli, Aude Sportisse, Francesco Cremonesi +2
Federated learning allows for the training of machine learning models on multiple decentralized local datasets without requiring explicit data exchange. However, data pre-processin…
Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism
Aude Sportisse, Hugo Schmutz, Olivier Humbert +2
Semi-supervised learning is a powerful technique for leveraging unlabeled data to improve machine learning models, but it can be affected by the presence of ``informative'' labels,…
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…
Explainability as statistical inference
Hugo Henri Joseph Senetaire, Damien Garreau, Jes Frellsen +1
A wide variety of model explanation approaches have been proposed in recent years, all guided by very different rationales and heuristics. In this paper, we take a new route and ca…
Generalised Mutual Information for Discriminative Clustering
Louis Ohl, Pierre-Alexandre Mattei, Charles Bouveyron +4
In the last decade, recent successes in deep clustering majorly involved the mutual information (MI) as an unsupervised objective for training neural networks with increasing regul…
A Multi-stage deep architecture for summary generation of soccer videos
Melissa Sanabria, Frédéric Precioso, Pierre-Alexandre Mattei +1
Video content is present in an ever-increasing number of fields, both scientific and commercial. Sports, particularly soccer, is one of the industries that has invested the most in…
Model-agnostic out-of-distribution detection using combined statistical tests
Federico Bergamin, Pierre-Alexandre Mattei, Jakob D. Havtorn +5
We present simple methods for out-of-distribution detection using a trained generative model. These techniques, based on classical statistical tests, are model-agnostic in the sens…
Asteroid Taxonomy from Cluster Analysis of Spectrometry and Albedo
Max Mahlke, Benoit Carry, Pierre-Alexandre Mattei
The classification of the minor bodies of the Solar System based on observables has been continuously developed and iterated over the past 40 years. While prior iterations followed…
Don't fear the unlabelled: safe semi-supervised learning via simple debiasing
Hugo Schmutz, Olivier Humbert, Pierre-Alexandre Mattei
Semi-supervised learning (SSL) provides an effective means of leveraging unlabelled data to improve a model performance. Even though the domain has received a considerable amount o…
Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives
Pierre-Alexandre Mattei, Jes Frellsen
We revisit the theory of importance weighted variational inference (IWVI), a promising strategy for learning latent variable models. IWVI uses new variational bounds, known as Mont…
Tensor decomposition for learning Gaussian mixtures from moments
Rima Khouja, Pierre-Alexandre Mattei, Bernard Mourrain
In data processing and machine learning, an important challenge is to recover and exploit models that can represent accurately the data. We consider the problem of recovering Gauss…
Unobserved classes and extra variables in high-dimensional discriminant analysis
Michael Fop, Pierre-Alexandre Mattei, Charles Bouveyron +1
In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase. Moreover, the same units in the test data may b…
not-MIWAE: Deep Generative Modelling with Missing not at Random Data
Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen
When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference. We present an app…