Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study. - Tom Huix; Szymon Majewski; Alain Durmus; Eric Moulines; Anna Korba (CoRR, 2022)

Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo. - Tom Huix; Matthew Zhang; Alain Durmus (AISTATS, 2023)

Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference. - Arnaud Descours; Tom Huix; Arnaud Guillin; Manon Michel; Éric Moulines; Boris Nectoux (COLT, 2023)

VITS : Variational Inference Thomson Sampling for contextual bandits. - Pierre Clavier; Tom Huix; Alain Durmus (CoRR, 2023)

VITS : Variational Inference Thompson Sampling for contextual bandits. - Pierre Clavier; Tom Huix; Alain Oliviero Durmus (ICML, 2024)

Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians. - Tom Huix; Anna Korba; Alain Oliviero Durmus; Eric Moulines (ICML, 2024)

Central Limit Theorem for Bayesian Neural Network trained with Variational Inference. - Arnaud Descours; Tom Huix; Arnaud Guillin; Manon Michel; Éric Moulines; Boris Nectoux (CoRR, 2024)