183 citations · 726 across the 47 of their papers we have counts for
6 papers · 1 filter
Balanced Training of Energy-Based Models with Adaptive Flow Sampling
Louis Grenioux, Éric Moulines, Marylou Gabrié
Energy-based models (EBMs) are versatile density estimation models that directly parameterize an unnormalized log density. Although very flexible, EBMs lack a specified normalizati…
FAVANO: Federated AVeraging with Asynchronous NOdes
Louis Leconte, Van Minh Nguyen, Eric Moulines
In this paper, we propose a novel centralized Asynchronous Federated Learning (FL) framework, FAVANO, for training Deep Neural Networks (DNNs) in resource-constrained environments.…
One-Step Distributional Reinforcement Learning
Mastane Achab, Reda Alami, Yasser Abdelaziz Dahou Djilali +2
Reinforcement learning (RL) allows an agent interacting sequentially with an environment to maximize its long-term expected return. In the distributional RL (DistrRL) paradigm, the…
Restarted Bayesian Online Change-point Detection for Non-Stationary Markov Decision Processes
Reda Alami, Mohammed Mahfoud, Eric Moulines
We consider the problem of learning in a non-stationary reinforcement learning (RL) environment, where the setting can be fully described by a piecewise stationary discrete-time Ma…
The Perturbed Prox-Preconditioned SPIDER algorithm for EM-based large scale learning
Gersende Fort, Eric Moulines
Incremental Expectation Maximization (EM) algorithms were introduced to design EM for the large scale learning framework by avoiding the full data set to be processed at each itera…
A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm
Gersende Fort, Eric Moulines, Hoi-To Wai
The Expectation Maximization (EM) algorithm is of key importance for inference in latent variable models including mixture of regressors and experts, missing observations. This pap…