88 citations · 487 across the 78 of their papers we have counts for
8 papers · 1 filter
Categorical Reparameterization with Denoising Diffusion models
Samson Gourevitch, Alain Durmus, Eric Moulines +2
Learning models with categorical variables requires optimizing expectations over discrete distributions, a setting in which stochastic gradient-based optimization is challenging du…
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…
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…
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…
Optimal Design for Reward Modeling in RLHF
Antoine Scheid, Etienne Boursier, Alain Durmus +4
Reinforcement Learning from Human Feedback (RLHF) has become a popular approach to align language models (LMs) with human preferences. This method involves collecting a large datas…
FedPop: A Bayesian Approach for Personalised Federated Learning
Nikita Kotelevskii, Maxime Vono, Eric Moulines +1
Personalised federated learning (FL) aims at collaboratively learning a machine learning model taylored for each client. Albeit promising advances have been made in this direction,…
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning
Maxime Vono, Vincent Plassier, Alain Durmus +2
The objective of Federated Learning (FL) is to perform statistical inference for data which are decentralised and stored locally on networked clients. FL raises many constraints wh…
Markov Decision Process for MOOC users behavioral inference
Firas Jarboui, Célya Gruson-daniel, Pierre Chanial +6
Studies on massive open online courses (MOOCs) users discuss the existence of typical profiles and their impact on the learning process of the students. However defining the typica…