6 citations · 7 across the 9 of their papers we have counts for
9 papers
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…
Rapidly Varying Completely Random Measures for Modeling Extremely Sparse Networks
Valentin Kilian, Benjamin Guedj, François Caron
Completely random measures (CRMs) are fundamental to Bayesian nonparametric models, with applications in clustering, feature allocation, and network analysis. A key quantity of int…
Closed-form Filtering for Non-linear Systems
Théophile Cantelobre, Carlo Ciliberto, Benjamin Guedj +1
Sequential Bayesian Filtering aims to estimate the current state distribution of a Hidden Markov Model, given the past observations. The problem is well-known to be intractable for…
Tighter Generalisation Bounds via Interpolation
Paul Viallard, Maxime Haddouche, Umut Şimşekli +1
This paper contains a recipe for deriving new PAC-Bayes generalisation bounds based on the -divergence, and, in addition, presents PAC-Bayes generalisation bounds where we…
A PAC-Bayesian Link Between Generalisation and Flat Minima
Maxime Haddouche, Paul Viallard, Umut Simsekli +1
Modern machine learning usually involves predictors in the overparameterised setting (number of trained parameters greater than dataset size), and their training yields not only go…
A note on regularised NTK dynamics with an application to PAC-Bayesian training
Eugenio Clerico, Benjamin Guedj
We establish explicit dynamics for neural networks whose training objective has a regularising term that constrains the parameters to remain close to their initial value. This keep…
Federated Learning with Nonvacuous Generalisation Bounds
Pierre Jobic, Maxime Haddouche, Benjamin Guedj
We introduce a novel strategy to train randomised predictors in federated learning, where each node of the network aims at preserving its privacy by releasing a local predictor but…
Comparing Comparators in Generalization Bounds
Fredrik Hellström, Benjamin Guedj
We derive generic information-theoretic and PAC-Bayesian generalization bounds involving an arbitrary convex comparator function, which measures the discrepancy between the trainin…
Generalization Bounds: Perspectives from Information Theory and PAC-Bayes
Fredrik Hellström, Giuseppe Durisi, Benjamin Guedj +1
A fundamental question in theoretical machine learning is generalization. Over the past decades, the PAC-Bayesian approach has been established as a flexible framework to address t…