103 citations · 187 across the 20 of their papers we have counts for
6 papers · 1 filter
T-Rep: Representation Learning for Time Series using Time-Embeddings
Archibald Fraikin, Adrien Bennetot, Stéphanie Allassonnière
Multivariate time series present challenges to standard machine learning techniques, as they are often unlabeled, high dimensional, noisy, and contain missing data. To address this…
Robust Reinforcement Learning with Distributional Risk-averse formulation
Pierre Clavier, Stéphanie Allassonière, Erwan Le Pennec
Robust Reinforcement Learning tries to make predictions more robust to changes in the dynamics or rewards of the system. This problem is particularly important when the dynamics an…
Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case
Clément Chadebec, Louis J. Vincent, Stéphanie Allassonnière
In recent years, deep generative models have attracted increasing interest due to their capacity to model complex distributions. Among those models, variational autoencoders have g…
Gaussian Graphical Model exploration and selection in high dimension low sample size setting
Thomas Lartigue, Simona Bottani, Stephanie Baron +3
Gaussian Graphical Models (GGM) are often used to describe the conditional correlations between the components of a random vector. In this article, we compare two families of GGM i…
Learning from both experts and data
Rémi Besson, Erwan Le Pennec, Stéphanie Allassonnière
In this work we study the problem of inferring a discrete probability distribution using both expert knowledge and empirical data. This is an important issue for many applications…
A Model-Based Reinforcement Learning Approach for a Rare Disease Diagnostic Task
Rémi Besson, Erwan Le Pennec, Stéphanie Allassonnière +3
In this work, we present our various contributions to the objective of building a decision support tool for the diagnosis of rare diseases. Our goal is to achieve a state of knowle…