6 papers
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 in the form of partial differential equations (PDE), into data-driven models. Despite strong em…
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…
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…
Provably Accurate Adaptive Sampling for Collocation Points in Physics-informed Neural Networks
Antoine Caradot, Rémi Emonet, Amaury Habrard +2
Despite considerable scientific advances in numerical simulation, efficiently solving PDEs remains a complex and often expensive problem. Physics-informed Neural Networks (PINN) ha…
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…