5 citations · 6 across the 4 of their papers we have counts for
4 papers
How good is PAC-Bayes at explaining generalisation?
Antoine Picard-Weibel, Eugenio Clerico, Roman Moscoviz +1
We discuss necessary conditions for a PAC-Bayes bound to provide a meaningful generalisation guarantee. Our analysis reveals that the optimal generalisation guarantee depends solel…
Learning via Surrogate PAC-Bayes
Antoine Picard-Weibel, Roman Moscoviz, Benjamin Guedj
PAC-Bayes learning is a comprehensive setting for (i) studying the generalisation ability of learning algorithms and (ii) deriving new learning algorithms by optimising a generalis…
Bayesian Uncertainty Quantification for Anaerobic Digestion models
Antoine Picard-Weibel, Gabriel Capson-Tojo, Benjamin Guedj +1
Uncertainty quantification is critical for ensuring adequate predictive power of computational models used in biology. Focusing on two anaerobic digestion models, this article intr…
Change of measure through the Legendre transform
Antoine Picard-Weibel, Benjamin Guedj
PAC-Bayes generalisation bounds are derived via change-of-measure inequalities that transfer concentration properties from a reference measure to all posterior measures. The specif…