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20142026
most citedNon-asymptotic convergence analysis for the Unadjusted Langevin Algorithm

88 citations · 487 across the 78 of their papers we have counts for

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Showing 2025Show all

8 papers · 1 filter

cs.CV2025

Efficient Zero-Shot Inpainting with Decoupled Diffusion Guidance

Badr Moufad, Navid Bagheri Shouraki, Alain Oliviero Durmus +4

Diffusion models have emerged as powerful priors for image editing tasks such as inpainting and local modification, where the objective is to generate realistic content that remain…

cs.LG2025

Briding Diffusion Posterior Sampling and Monte Carlo methods: a survey

Yazid Janati, Alain Durmus, Jimmy Olsson +1

Diffusion models enable the synthesis of highly accurate samples from complex distributions and have become foundational in generative modeling. Recently, they have demonstrated si…

cs.LG2025

Conditional Diffusion Models with Classifier-Free Gibbs-like Guidance

Badr Moufad, Yazid Janati, Alain Durmus +3

Classifier-Free Guidance (CFG) is a widely used technique for improving conditional diffusion models by linearly combining the outputs of conditional and unconditional denoisers. W…

cs.LG2025

Online Decision-Focused Learning

Aymeric Capitaine, Maxime Haddouche, Eric Moulines +3

Decision-focused learning (DFL) is an increasingly popular paradigm for training predictive models whose outputs are used in decision-making tasks. Instead of merely optimizing for…

stat.ML2025

Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up

Paul Mangold, Alain Durmus, Aymeric Dieuleveut +1

This paper proposes a novel analysis for the Scaffold algorithm, a popular method for dealing with data heterogeneity in federated learning. While its convergence in deterministic…

stat.ML2025★ 1 cited

A Mixture-Based Framework for Guiding Diffusion Models

Yazid Janati, Badr Moufad, Mehdi Abou El Qassime +3

Denoising diffusion models have driven significant progress in the field of Bayesian inverse problems. Recent approaches use pre-trained diffusion models as priors to solve a wide…

cs.GT2025★ 1 cited

Online Decision-Making in Tree-Like Multi-Agent Games with Transfers

Antoine Scheid, Etienne Boursier, Alain Durmus +2

The widespread deployment of Machine Learning systems everywhere raises challenges, such as dealing with interactions or competition between multiple learners. In that goal, we stu…

cs.GT2025

Prediction-Aware Learning in Multi-Agent Systems

Aymeric Capitaine, Etienne Boursier, Eric Moulines +2

The framework of uncoupled online learning in multiplayer games has made significant progress in recent years. In particular, the development of time-varying games has considerably…