5 citations · 5 across the 3 of their papers we have counts for
3 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…