Tom Huix

According to our database, Tom Huix authored at least 7 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

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

Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians.

Tom Huix, Anna Korba, Alain Oliviero Durmus, Eric Moulines

Proceedings of the Forty-first International Conference on Machine Learning, 2024

VITS : Variational Inference Thompson Sampling for contextual bandits.

Pierre Clavier, Tom Huix, Alain Oliviero Durmus

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

VITS : Variational Inference Thomson Sampling for contextual bandits.

Pierre Clavier, Tom Huix, Alain Durmus

CoRR, 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

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo.

Tom Huix, Matthew Zhang, Alain Durmus

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study.

Tom Huix, Szymon Majewski, Alain Durmus, Eric Moulines, Anna Korba

CoRR, 2022