49 citations · 412 across the 50 of their papers we have counts for
50 papers
Learning via Wasserstein-Based High Probability Generalisation Bounds
Paul Viallard, Maxime Haddouche, Umut Şimşekli +1
Minimising upper bounds on the population risk or the generalisation gap has been widely used in structural risk minimisation (SRM) -- this is in particular at the core of PAC-Baye…
Wasserstein PAC-Bayes Learning: Exploiting Optimisation Guarantees to Explain Generalisation
Maxime Haddouche, Benjamin Guedj
PAC-Bayes learning is an established framework to both assess the generalisation ability of learning algorithms, and design new learning algorithm by exploiting generalisation boun…
Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty
Felix Biggs, Benjamin Guedj
We introduce a modified version of the excess risk, which can be used to obtain tighter, fast-rate PAC-Bayesian generalisation bounds. This modified excess risk leverages informati…
PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales
Maxime Haddouche, Benjamin Guedj
While PAC-Bayes is now an established learning framework for light-tailed losses (\emph{e.g.}, subgaussian or subexponential), its extension to the case of heavy-tailed losses rema…
Generalisation under gradient descent via deterministic PAC-Bayes
Eugenio Clerico, Tyler Farghly, George Deligiannidis +2
We establish disintegrated PAC-Bayesian generalisation bounds for models trained with gradient descent methods or continuous gradient flows. Contrary to standard practice in the PA…
Opening up echo chambers via optimal content recommendation
Antoine Vendeville, Anastasios Giovanidis, Effrosyni Papanastasiou +1
Online social platforms have become central in the political debate. In this context, the existence of echo chambers is a problem of primary relevance. These clusters of like-minde…