activity
20182022
most citedPartially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation

14 citations · 19 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CV2022

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…

stat.ML20222 cited

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…

stat.ML2022

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…

stat.ME20213 cited

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…

stat.ML2020

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…

stat.ME2019

A Parsimonious Tour of Bayesian Model Uncertainty

Pierre-Alexandre Mattei

Modern statistical software and machine learning libraries are enabling semi-automated statistical inference. Within this context, it appears easier and easier to try and fit many…