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Éric Moulines

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Central Limit Theorem for Bayesian Neural Network trained with Variational Inference

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Jun 10, 2024
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Inexact subgradient methods for semialgebraic functions

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Apr 30, 2024
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Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference

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Jul 10, 2023
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Balanced Training of Energy-Based Models with Adaptive Flow Sampling

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Jun 21, 2023
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On Sampling with Approximate Transport Maps

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Feb 09, 2023
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On Riemannian Stochastic Approximation Schemes with Fixed Step-Size

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Feb 19, 2021
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Convergence Analysis of Riemannian Stochastic Approximation Schemes

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Jun 15, 2020
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On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation

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Jan 22, 2020
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Unifying mirror descent and dual averaging

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Oct 30, 2019
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On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case

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May 30, 2019
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