Variational inference for Bayesian Neural Networks
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Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study . T Huix, S Majewski, A Durmus, E Moulines, A Korba. [Paper]
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Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference . A. Descours, T. Huix, A. Guillin, M. Michel, E. Moulines, B. Nectoux. (COLT 2023). [Paper]
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Central Limit Theorem for Bayesian Neural Networks trained with Variational Inference . A. Descours, T. Huix, A. Guillin, M. Michel, E. Moulines, B. Nectoux.
Contextual Bandit
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Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo . T. Huix, M. Zhang, A. Durmus. (ICML 2024). [Paper]
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VITS: Variational Inference Thomson Sampling for contextual bandits . T. Huix, P. Clavier, A. Durmus. (AISTAT 2023). [Paper]
Bayesian Sampling
- Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians . T. Huix, A. Korba, A. Durmus, E. Moulines. (ICML 2024).