Moulines, Eric

75 publications

ICML 2025 A Mixture-Based Framework for Guiding Diffusion Models Yazid Janati, Badr Moufad, Mehdi Abou El Qassime, Alain Oliviero Durmus, Eric Moulines, Jimmy Olsson

AISTATS 2025 Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents Safwan Labbi, Daniil Tiapkin, Lorenzo Mancini, Paul Mangold, Eric Moulines

ICML 2025 Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean Field Games Antonio Ocello, Daniil Tiapkin, Lorenzo Mancini, Mathieu Lauriere, Eric Moulines

ICLR 2025 From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation Nikita Kotelevskii, Vladimir Kondratyev, Martin Takáč, Eric Moulines, Maxim Panov

ICLR 2025 Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson–Romberg Extrapolation Marina Sheshukova, Denis Belomestny, Alain Oliviero Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov

ICML 2025 Prediction-Aware Learning in Multi-Agent Systems Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Oliviero Durmus

ICLR 2025 Probabilistic Conformal Prediction with Approximate Conditional Validity Vincent Plassier, Alexander Fishkov, Mohsen Guizani, Maxim Panov, Eric Moulines

ICLRW 2025 ReALLM: A General Framework for LLM Compression and Fine-Tuning Lisa Bedin, Louis Leconte, Van Minh Nguyen, Eric Moulines

ICML 2025 Rectifying Conformity Scores for Better Conditional Coverage Vincent Plassier, Alexander Fishkov, Victor Dheur, Mohsen Guizani, Souhaib Ben Taieb, Maxim Panov, Eric Moulines

AISTATS 2025 Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation Paul Mangold, Alain Oliviero Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines

ICML 2025 Scaffold with Stochastic Gradients: New Analysis with Linear Speed-up Paul Mangold, Alain Oliviero Durmus, Aymeric Dieuleveut, Eric Moulines

NeurIPS 2025 Statistical Inference for Linear Stochastic Approximation with Markovian Noise Sergey Samsonov, Marina Sheshukova, Eric Moulines, Alexey Naumov

ICLR 2025 Variational Diffusion Posterior Sampling with Midpoint Guidance Badr Moufad, Yazid Janati, Lisa Bedin, Alain Oliviero Durmus, Randal Douc, Eric Moulines, Jimmy Olsson

ICLR 2024 Demonstration-Regularized RL Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Menard

NeurIPS 2024 Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors Yazid Janati, Badr Moufad, Alain Durmus, Eric Moulines, Jimmy Olsson

AISTATS 2024 Efficient Conformal Prediction Under Data Heterogeneity Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horvath, Martin Takac, Eric Moulines, Maxim Panov

NeurIPS 2024 Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning Sergey Samsonov, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov

COLT 2024 Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines

ICML 2024 Incentivized Learning in Principal-Agent Bandit Games Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, Eric Moulines, Michael Jordan, El-Mahdi El-Mhamdi, Alain Oliviero Durmus

NeurIPS 2024 Learning to Mitigate Externalities: The Coase Theorem with Hindsight Rationality Antoine Scheid, Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus

NeurIPS 2024 Leveraging an ECG Beat Diffusion Model for Morphological Reconstruction from Indirect Signals Lisa Bedin, Gabriel Cardoso, Josselin Duchateau, Remi Dubois, Eric Moulines

ICLR 2024 Monte Carlo Guided Denoising Diffusion Models for Bayesian Linear Inverse Problems. Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines

NeurIPS 2024 Piecewise Deterministic Generative Models Andrea Bertazzi, Dario Shariatian, Umut Simsekli, Eric Moulines, Alain Durmus

AISTATS 2024 Queuing Dynamics of Asynchronous Federated Learning Louis Leconte, Matthieu Jonckheere, Sergey Samsonov, Eric Moulines

JMLR 2024 Rates of Convergence for Density Estimation with Generative Adversarial Networks Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov

NeurIPS 2024 SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Reda Alami, Alexey Naumov, Eric Moulines

ICML 2024 Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians Tom Huix, Anna Korba, Alain Oliviero Durmus, Eric Moulines

NeurIPS 2024 Unravelling in Collaborative Learning Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus

AISTATS 2023 ASkewSGD : An Annealed Interval-Constrained Optimisation Method to Train Quantized Neural Networks Louis Leconte, Sholom Schechtman, Eric Moulines

ICMLW 2023 Balanced Training of Energy-Based Models with Adaptive Flow Sampling Louis Grenioux, Eric Moulines, Marylou Gabrié

ICML 2023 Conformal Prediction for Federated Uncertainty Quantification Under Label Shift Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov

NeurIPSW 2023 ECG Inpainting with Denoising Diffusion Prior Lisa Bedin, Gabriel Cardoso, Remi Dubois, Eric Moulines

ICML 2023 Fast Rates for Maximum Entropy Exploration Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Remi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Menard

AISTATS 2023 Federated Averaging Langevin Dynamics: Toward a Unified Theory and New Algorithms Vincent Plassier, Eric Moulines, Alain Durmus

NeurIPS 2023 First Order Methods with Markovian Noise: From Acceleration to Variational Inequalities Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander Gasnikov, Alexey Naumov, Eric Moulines

COLT 2023 Law of Large Numbers for Bayesian Two-Layer Neural Network Trained with Variational Inference Arnaud Descours, Tom Huix, Arnaud Guillin, Manon Michel, Éric Moulines, Boris Nectoux

NeurIPS 2023 Model-Free Posterior Sampling via Learning Rate Randomization Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Remi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

ICML 2023 On Sampling with Approximate Transport Maps Louis Grenioux, Alain Oliviero Durmus, Eric Moulines, Marylou Gabrié

TMLR 2023 One-Step Distributional Reinforcement Learning Mastane Achab, Reda Alami, Yasser Abdelaziz Dahou Djilali, Kirill Fedyanin, Eric Moulines

COLT 2023 Orthogonal Directions Constrained Gradient Method: From Non-Linear Equality Constraints to Stiefel Manifold Sholom Schechtman, Daniil Tiapkin, Michael Muehlebach, Éric Moulines

ICML 2023 Quantile Credit Assignment Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Remi Munos

CoLLAs 2023 Restarted Bayesian Online Change-Point Detection for Non-Stationary Markov Decision Processes Reda Alami, Mohammed Mahfoud, Eric Moulines

ICML 2023 State and Parameter Learning with PARIS Particle Gibbs Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines, Jimmy Olsson

AISTATS 2022 QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines

NeurIPS 2022 BR-SNIS: Bias Reduced Self-Normalized Importance Sampling Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson

ICML 2022 Diffusion Bridges Vector Quantized Variational Autoencoders Max Cohen, Guillaume Quispe, Sylvain Le Corff, Charles Ollion, Eric Moulines

NeurIPSW 2022 Distributional Deep Q-Learning with CVaR Regression Mastane Achab, Reda Alami, Yasser Abdelaziz Dahou Djilali, Kirill Fedyanin, Eric Moulines, Maxim Panov

NeurIPS 2022 FedPop: A Bayesian Approach for Personalised Federated Learning Nikita Kotelevskii, Maxime Vono, Alain Durmus, Eric Moulines

ICML 2022 From Dirichlet to Rubin: Optimistic Exploration in RL Without Bonuses Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Menard

NeurIPS 2022 Local-Global MCMC Kernels: The Best of Both Worlds Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines

ALT 2022 Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex Problems Belhal Karimi, Hoi-To Wai, Eric Moulines, Ping Li

NeurIPS 2022 Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Remi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard

AISTATS 2021 On Riemannian Stochastic Approximation Schemes with Fixed Step-Size Alain Durmus, Pablo Jiménez, Eric Moulines, Salem Said

ICML 2021 Counterfactual Credit Assignment in Model-Free Reinforcement Learning Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas S Stepleton, Nicolas Heess, Arthur Guez, Eric Moulines, Marcus Hutter, Lars Buesing, Remi Munos

ICML 2021 DG-LMC: A Turn-Key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo Within Gibbs Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines

NeurIPS 2021 Federated-EM with Heterogeneity Mitigation and Variance Reduction Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Geneviève Robin

ICML 2021 Monte Carlo Variational Auto-Encoders Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov

NeurIPS 2021 NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform Achille Thin, Yazid Janati El Idrissi, Sylvain Le Corff, Charles Ollion, Eric Moulines, Arnaud Doucet, Alain Durmus, Christian X Robert

COLT 2021 On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Hoi-To Wai

NeurIPS 2021 Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai

NeurIPS 2020 A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm Gersende Fort, Eric Moulines, Hoi-To Wai

ICML 2020 Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations Robert Mattila, Cristian Rojas, Eric Moulines, Vikram Krishnamurthy, Bo Wahlberg

COLT 2020 Finite Time Analysis of Linear Two-Timescale Stochastic Approximation with Markovian Noise Maxim Kaledin, Eric Moulines, Alexey Naumov, Vladislav Tadic, Hoi-To Wai

COLT 2019 Non-Asymptotic Analysis of Biased Stochastic Approximation Scheme Belhal Karimi, Blazej Miasojedow, Eric Moulines, Hoi-To Wai

NeurIPS 2019 On the Global Convergence of (Fast) Incremental Expectation Maximization Methods Belhal Karimi, Hoi-To Wai, Eric Moulines, Marc Lavielle

NeurIPS 2018 Low-Rank Interaction with Sparse Additive Effects Model for Large Data Frames Geneviève Robin, Hoi-To Wai, Julie Josse, Olga Klopp, Eric Moulines

NeurIPS 2018 The Promises and Pitfalls of Stochastic Gradient Langevin Dynamics Nicolas Brosse, Alain Durmus, Eric Moulines

JMLR 2017 On Perturbed Proximal Gradient Algorithms Yves F. Atchadé, Gersende Fort, Eric Moulines

COLT 2017 Sampling from a Log-Concave Distribution with Compact Support with Proximal Langevin Monte Carlo Nicolas Brosse, Alain Durmus, Éric Moulines, Marcelo Pereyra

NeurIPS 2016 Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël Richard

NeurIPS 2014 Probabilistic Low-Rank Matrix Completion on Finite Alphabets Jean Lafond, Olga Klopp, Eric Moulines, Joseph Salmon

NeurIPS 2013 Non-Strongly-Convex Smooth Stochastic Approximation with Convergence Rate O(1/n) Francis Bach, Eric Moulines

NeurIPS 2011 Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning Eric Moulines, Francis R. Bach

ALT 2011 On Upper-Confidence Bound Policies for Switching Bandit Problems Aurélien Garivier, Eric Moulines

NeurIPS 2008 Kernel Change-Point Analysis Zaïd Harchaoui, Eric Moulines, Francis R. Bach