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2020 – today
- 2024
[c4]Pierre Clavier, Tom Huix, Alain Oliviero Durmus:
VITS : Variational Inference Thompson Sampling for contextual bandits. ICML 2024- [c3]Tom Huix, Anna Korba, Alain Oliviero Durmus, Eric Moulines:
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians. ICML 2024 - [i4]Tom Huix, Anna Korba, Alain Durmus, Eric Moulines:
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians. CoRR abs/2406.04012 (2024) - [i3]Arnaud Descours, Tom Huix, Arnaud Guillin, Manon Michel, Éric Moulines, Boris Nectoux:
Central Limit Theorem for Bayesian Neural Network trained with Variational Inference. CoRR abs/2406.09048 (2024) - 2023
- [c2]Tom Huix, Matthew Zhang, Alain Durmus:
Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo. AISTATS 2023: 8749-8770 - [c1]Arnaud Descours, Tom Huix, Arnaud Guillin, Manon Michel, Éric Moulines, Boris Nectoux:
Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference. COLT 2023: 4657-4695 - [i2]Pierre Clavier, Tom Huix, Alain Durmus:
VITS : Variational Inference Thomson Sampling for contextual bandits. CoRR abs/2307.10167 (2023) - 2022
- [i1]Tom Huix, Szymon Majewski, Alain Durmus, Eric Moulines, Anna Korba:
Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study. CoRR abs/2207.03859 (2022)
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