Eric Moulines

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