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stat.ML2026

Revisiting the Sliced Wasserstein Kernel for persistence diagrams: a Figalli-Gigli approach

Marc Janthial, Théo Lacombe

The Sliced Wasserstein Kernel (SWK) for persistence diagrams was introduced in (Carri{è}re et al. 2017) as a powerful tool to implicitly embed persistence diagrams in a Hilbert spa…

stat.ML2024

Central Limit Theorem for Bayesian Neural Network trained with Variational Inference

Arnaud Descours, Tom Huix, Arnaud Guillin +3

In this paper, we rigorously derive Central Limit Theorems (CLT) for Bayesian two-layerneural networks in the infinite-width limit and trained by variational inference on a regress…