most citedWasserstein PAC-Bayes Learning: Exploiting Optimisation Guarantees to Explain Generalisation

2 citations · 2 across the 2 of their papers we have counts for

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2 papers

stat.ML2024

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…

stat.ML2023★ 2 cited

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…