← Authors

Eric Moulines

69 papers · 2008–2026 · 9 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (8) 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (17) πŸ—ΊοΈ Taxonomy Completionist (25) 🐝 Cross-Pollinator (14) 🏠 Conference Loyalist (25) πŸ”¬ Deep Specialist (15) πŸ‘‘ Triple Crown πŸ† Keyword Champion πŸ† Grand Slam 🌱 Topic Pioneer 🀝 Dynamic Duo (17) πŸ”₯ Unstoppable (10) ⚑ Prolific Year (15) πŸ’Ž Century Club (68) πŸ—ƒοΈ Keyword Collector (80) πŸ“ˆ Trend Setter

Conferences

NIPS (25) ICML (18) AISTATS (8) COLT (7) ICLR (6) JMLR (2) AAAI (1) ALT (1) COLING (1)

Top co-authors

Alain Durmus (17) Alexey Naumov (15) Sergey Samsonov (13) Daniil Tiapkin (10) Alain Oliviero Durmus (9) Hoi-To Wai (8) Vincent Plassier (7) Denis Belomestny (7) Michal Valko (6) Maxim Panov (6)

Keywords

markov chain monte carlo (14) bayesian inference (9) federated learning (8) variance reduction (7) stochastic approximation (7) stochastic optimization (5) temporal difference learning (4) posterior sampling (4) latent variable model (4) distributed learning (3) polyak-ruppert averaging (3) stochastic gradient descent (3) policy gradient (3) uncertainty quantification (3) markov chain (3) variational inference (3) stochastic gradient (3) convex optimization (3) langevin dynamics (3) markov decision process (3)

Papers

Loss-Guided Auxiliary Agents for Overcoming Mode Collapse in GFlowNets AAAI 2026
Rectifying Conformity Scores for Better Conditional Coverage ICML 2025
Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean Field Games ICML 2025
Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up ICML 2025
A Mixture-Based Framework for Guiding Diffusion Models ICML 2025
Prediction-Aware Learning in Multi-Agent Systems ICML 2025
From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation ICLR 2025
Variational Diffusion Posterior Sampling with Midpoint Guidance ICLR 2025
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson–Romberg Extrapolation ICLR 2025
Probabilistic Conformal Prediction with Approximate Conditional Validity ICLR 2025
Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect COLING 2025
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents AISTATS 2025
Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation AISTATS 2025
Incentivized Learning in Principal-Agent Bandit Games ICML 2024
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning NIPS 2024
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning NIPS 2024
Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality NIPS 2024
Leveraging an ECG Beat Diffusion Model for Morphological Reconstruction from Indirect Signals NIPS 2024
Piecewise deterministic generative models NIPS 2024
Unravelling in Collaborative Learning NIPS 2024
Divide-and-Conquer Posterior Sampling for Denoising Diffusion priors NIPS 2024
Queuing dynamics of asynchronous Federated Learning AISTATS 2024
Efficient Conformal Prediction under Data Heterogeneity AISTATS 2024
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability COLT 2024
Demonstration-Regularized RL ICLR 2024
Monte Carlo guided Denoising Diffusion models for Bayesian linear inverse problems. ICLR 2024
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians ICML 2024
Rates of convergence for density estimation with generative adversarial networks JMLR 2024
Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold COLT 2023
Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference COLT 2023
State and parameter learning with PARIS particle Gibbs ICML 2023
ASkewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks AISTATS 2023
Fast Rates for Maximum Entropy Exploration ICML 2023
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms AISTATS 2023
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift ICML 2023
Quantile Credit Assignment ICML 2023
On Sampling with Approximate Transport Maps ICML 2023
Model-free Posterior Sampling via Learning Rate Randomization NIPS 2023
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities NIPS 2023
Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex Problems ALT 2022
Local-Global MCMC kernels: the best of both worlds NIPS 2022
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling NIPS 2022
Diffusion bridges vector quantized variational autoencoders ICML 2022
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses ICML 2022
FedPop: A Bayesian Approach for Personalised Federated Learning NIPS 2022
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees NIPS 2022
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning AISTATS 2022
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size AISTATS 2021
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning COLT 2021
Counterfactual Credit Assignment in Model-Free Reinforcement Learning ICML 2021
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs ICML 2021
Monte Carlo Variational Auto-Encoders ICML 2021
NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform NIPS 2021
Federated-EM with heterogeneity mitigation and variance reduction NIPS 2021
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize NIPS 2021
A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm NIPS 2020
Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations ICML 2020
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise COLT 2020
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods NIPS 2019
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme COLT 2019
The promises and pitfalls of Stochastic Gradient Langevin Dynamics NIPS 2018
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames NIPS 2018
On Perturbed Proximal Gradient Algorithms JMLR 2017
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo COLT 2017
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo NIPS 2016
Probabilistic low-rank matrix completion on finite alphabets NIPS 2014
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) NIPS 2013
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning NIPS 2011
Kernel Change-point Analysis NIPS 2008