4 papers
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…
Generalization Bounds for Markov Algorithms through Entropy Flow Computations
Benjamin Dupuis, Maxime Haddouche, George Deligiannidis +1
Many learning algorithms can be represented as Markov processes, and understanding their generalization error is a central topic in learning theory. For specific continuous-time no…
Online (Non-)Convex Learning via Tempered Optimism
Maxime Haddouche, Olivier Wintenberger, Benjamin Guedj
Optimistic Online Learning aims to exploit experts conveying reliable information to predict the future. However, such implicit optimism may be challenged when it comes to practica…
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…