523 citations · 897 across the 41 of their papers we have counts for
43 papers
On the disintegration of the stochastic majority vote: From PAC-Bayesian bounds to a self-bounding algorithm
Julien Bastian, Benjamin Leblanc, Pascal Germain +4
Weighted majority votes are central to many successful ensemble methods. PAC-Bayesian theory provides tight generalization guarantees for such models by analyzing the expected risk…
On the post-hoc Evaluation of PDE Discovery: A Multifaceted Challenge of Scientific Advancement
Baptiste Mathevon, Farah Cherfaoui, Amaury Habrard +1
Partial differential equation (PDE) discovery aims to identify from data the governing law of a physical system. Constituting a cornerstone of scientific advancement, it has become…
A PAC-Bayesian View of Generalisation for Physics-Informed Machine Learning
Thien V. Nguyen, Amaury Habrard, Benjamin Guedj
Physics-informed machine learning (PIML) integrates mechanistic knowledge, typically through partial differential equations (PDEs), into data-driven models. Despite strong empirica…
PAC-Bayesian Generalization Guarantees for Fairness on Stochastic and Deterministic Classifiers
Julien Bastian, Benjamin Leblanc, Pascal Germain +5
Classical PAC generalization bounds on the prediction risk of a classifier are insufficient to provide theoretical guarantees on fairness when the goal is to learn models balancing…
From GNNs to Symbolic Surrogates via Kolmogorov-Arnold Networks for Delay Prediction
Sami Marouani, Kamal Singh, Baptiste Jeudy +1
Accurate prediction of flow delay is essential for optimizing and managing modern communication networks. We investigate three levels of modeling for this task. First, we implement…
Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks
Kamal Singh, Sami Marouani, Ahmad Al Sheikh +2
Reinforcement learning (RL) has been increasingly applied to network control problems, such as load balancing. However, existing RL approaches often suffer from lack of interpretab…