62 citations · 175 across the 40 of their papers we have counts for
43 papers
On the instability of local learning algorithms: Q-learning can fail in infinite state spaces
Urtzi Ayesta, Sergey Foss, Matthieu Jonckheere +1
We investigate the challenges of applying model-free reinforcement learning algorithms, like online Q-learning, to infinite state space Markov Decision Processes (MDPs). We first i…
Decision-Epoch Matters: Unveiling its Impact on the Stability of Scheduling with Randomly Varying Connectivity
Nahuel Soprano-Loto, Urtzi Ayesta, Matthieu Jonckheere +1
A classical queuing theory result states that in a parallel-queue single-server model, the maximum stability region does not depend on the scheduling decision epochs, and in partic…
Choosing the parameter of the Fermat distance: navigating geometry and noise
Frédéric Chazal, Laure Ferraris, Pablo Groisman +3
The Fermat distance has been recently established as a useful tool for machine learning tasks when a natural distance is not directly available to the practitioner or to improve th…
Algorithme EM régularisé
Pierre Houdouin, Matthieu Jonkcheere, Frederic Pascal
Expectation-Maximization (EM) algorithm is a widely used iterative algorithm for computing maximum likelihood estimate when dealing with Gaussian Mixture Model (GMM). When the samp…
FEMDA: Une méthode de classification robuste et flexible
Pierre Houdouin, Matthieu Jonckheere, Frederic Pascal
Linear and Quadratic Discriminant Analysis (LDA and QDA) are well-known classical methods but can heavily suffer from non-Gaussian distributions and/or contaminated datasets, mainl…
Online matching for the multiclass stochastic block model
Nahuel Soprano-Loto, Matthieu Jonckheere, Pascal Moyal
We consider the problem of sequential matching in a stochastic block model with several classes of nodes and generic compatibility constraints. When the probabilities of connection…