Eric Moulines

ORCID

Publication Activity (10 Years)

Years Active: 1987-2024
Publications (10 Years): 142

Top Venues

Publications

Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus
Unravelling in Collaborative Learning. CoRR (2024)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Demonstration-Regularized RL. ICLR (2024)
Sergey Samsonov, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning. CoRR (2024)
Yazid Janati El Idrissi, Alain Durmus, Eric Moulines, Jimmy Olsson
Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors. CoRR (2024)
Gabriel Victorino Cardoso, Lisa Bedin, Josselin Duchateau, Rémi Dubois, Eric Moulines
Bayesian ECG reconstruction using denoising diffusion generative models. CoRR (2024)
Andrea Bertazzi, Alain Oliviero Durmus, Dario Shariatian, Umut Simsekli, Eric Moulines
Piecewise deterministic generative models. CoRR (2024)
Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov
Efficient Conformal Prediction under Data Heterogeneity. AISTATS (2024)
Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines
Monte Carlo guided Denoising Diffusion models for Bayesian linear inverse problems. ICLR (2024)
Louis Leconte, Van Minh Nguyen, Eric Moulines
FAVANO: Federated Averaging with Asynchronous Nodes. ICASSP (2024)
Vincent Plassier, Alexander Fishkov, Maxim Panov, Eric Moulines
Conditionally valid Probabilistic Conformal Prediction. CoRR (2024)
Tom Huix, Anna Korba, Alain Durmus, Eric Moulines
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians. CoRR (2024)
Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, El Mahdi El Mhamdi, Eric Moulines, Michael I. Jordan, Alain Durmus
Incentivized Learning in Principal-Agent Bandit Games. CoRR (2024)
Louis Leconte, Matthieu Jonckheere, Sergey Samsonov, Eric Moulines
Queuing dynamics of asynchronous Federated Learning. AISTATS (2024)
Louis Leconte, Matthieu Jonckheere, Sergey Samsonov, Eric Moulines
Queuing dynamics of asynchronous Federated Learning. CoRR (2024)
Antoine Scheid, Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus
Mitigating Externalities while Learning: an Online Version of the Coase Theorem. CoRR (2024)
Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Réda Alami, Alexey Naumov, Eric Moulines
SCAFFLSA: Quantifying and Eliminating Heterogeneity Bias in Federated Linear Stochastic Approximation and Temporal Difference Learning. CoRR (2024)
Louis Leconte, Lisa Bedin, Van Minh Nguyen, Eric Moulines
ReALLM: A general framework for LLM compression and fine-tuning. CoRR (2024)
Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability. COLT (2024)
Fouzi Boukhalfa, Réda Alami, Mastane Achab, Eric Moulines, Mehdi Bennis, Thierry Lestable
Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study. ICC Workshops (2024)
Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander V. Gasnikov, Alexey Naumov, Eric Moulines
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities. NeurIPS (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Demonstration-Regularized RL. CoRR (2023)
Mastane Achab, Réda Alami, Yasser Abdelaziz Dahou Djilali, Kirill Fedyanin, Eric Moulines
One-Step Distributional Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023)
Vincent Plassier, Eric Moulines, Alain Durmus
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. AISTATS (2023)
Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. CoRR (2023)
Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Hoi-To Wai
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning. IEEE Trans. Signal Process. 71 (2023)
Mastane Achab, Réda Alami, Yasser Abdelaziz Dahou Djilali, Kirill Fedyanin, Eric Moulines
One-Step Distributional Reinforcement Learning. CoRR (2023)
Gersende Fort, Eric Moulines
Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization. CoRR (2023)
Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander V. Gasnikov, Alexey Naumov, Eric Moulines
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities. CoRR (2023)
Réda Alami, Mohammed Mahfoud, Eric Moulines
Restarted Bayesian Online Change-point Detection for Non-Stationary Markov Decision Processes. CoRR (2023)
Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines
Finite-Sample Analysis of the Temporal Difference Learning. CoRR (2023)
Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines
Monte Carlo guided Diffusion for Bayesian linear inverse problems. CoRR (2023)
Gersende Fort, Eric Moulines
Stochastic variable metric proximal gradient with variance reduction for non-convex composite optimization. Stat. Comput. 33 (3) (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
Fast Rates for Maximum Entropy Exploration. CoRR (2023)
Fouzi Boukhalfa, Réda Alami, Mastane Achab, Eric Moulines, Mehdi Bennis
Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study. CoRR (2023)
Réda Alami, Mohammed Mahfoud, Eric Moulines
Restarted Bayesian Online Change-point Detection for Non-Stationary Markov Decision Processes. CoLLAs (2023)
Louis Leconte, Sholom Schechtman, Eric Moulines
ASkewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks. AISTATS (2023)
Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines, Jimmy Olsson
State and parameter learning with PARIS particle Gibbs. ICML (2023)
Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, Rémi Munos
Quantile Credit Assignment. ICML (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Model-free Posterior Sampling via Learning Rate Randomization. CoRR (2023)
Louis Leconte, Van Minh Nguyen, Eric Moulines
FAVAS: Federated AVeraging with ASynchronous clients. CoRR (2023)
Vincent Plassier, Nikita Kotelevskii, Aleksandr Rubashevskii, Fedor Noskov, Maksim Velikanov, Alexander Fishkov, Samuel Horváth, Martin Takác, Eric Moulines, Maxim Panov
Efficient Conformal Prediction under Data Heterogeneity. CoRR (2023)
Louis Leconte, Van Minh Nguyen, Eric Moulines
Federated Boolean Neural Networks Learning. FMEC (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
Fast Rates for Maximum Entropy Exploration. ICML (2023)
Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. ICML (2023)
Louis Grenioux, Alain Oliviero Durmus, Eric Moulines, Marylou Gabrié
On Sampling with Approximate Transport Maps. ICML (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
Model-free Posterior Sampling via Learning Rate Randomization. NeurIPS (2023)
Vincent Plassier, Alain Durmus, Eric Moulines
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. CoRR (2022)
Nikita Kotelevskii, Maxime Vono, Eric Moulines, Alain Durmus
FedPop: A Bayesian Approach for Personalised Federated Learning. CoRR (2022)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS (2022)
Max Cohen, Guillaume Quispe, Sylvain Le Corff, Charles Ollion, Eric Moulines
Diffusion bridges vector quantized variational autoencoders. ICML (2022)
Denis Belomestny, Eric Moulines, Sergey Samsonov
Variance reduction for additive functionals of Markov chains via martingale representations. Stat. Comput. 32 (1) (2022)
Gabriel Victorino Cardoso, Geneviève Robin, Andony Arrieula, Mark Potse, Michel Haïssaguerre, Eric Moulines, Rémi Dubois
A Patient-Specific Single Equivalent Dipole Model. CinC (2022)
Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov
Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation. CoRR (2022)
Louis Leconte, Sholom Schechtman, Eric Moulines
AskewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks. CoRR (2022)
Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling. CoRR (2022)
Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines
Local-Global MCMC kernels: the best of both worlds. NeurIPS (2022)
Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling. NeurIPS (2022)
Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. AISTATS (2022)
Belhal Karimi, Hoi-To Wai, Eric Moulines, Ping Li
Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex Problems. ALT (2022)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR (2022)
Nikita Kotelevskii, Maxime Vono, Alain Durmus, Eric Moulines
FedPop: A Bayesian Approach for Personalised Federated Learning. NeurIPS (2022)
Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. CoRR (2022)
Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. ICML (2022)
Tom Huix, Szymon Majewski, Alain Durmus, Eric Moulines, Anna Korba
Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study. CoRR (2022)
Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize. NeurIPS (2021)
Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov
Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC. SIAM/ASA J. Uncertain. Quantification 9 (2) (2021)
Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm. CoRR (2021)
Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines
MCMC: Sampling through Exploration Exploitation. CoRR (2021)
Alain Durmus, Pablo Jiménez, Eric Moulines, Salem Said
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size. AISTATS (2021)
Gersende Fort, Eric Moulines
The Perturbed Prox-Preconditioned SPIDER algorithm for EM-based large scale learning. CoRR (2021)
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, Rémi Munos
Counterfactual Credit Assignment in Model-Free Reinforcement Learning. ICML (2021)
Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs. ICML (2021)
Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov
Monte Carlo Variational Auto-Encoders. CoRR (2021)
Gersende Fort, Eric Moulines
The Perturbed Prox-Preconditioned Spider Algorithm: Non-Asymptotic Convergence Bounds. SSP (2021)
Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Geneviève Robin
Federated Expectation Maximization with heterogeneity mitigation and variance reduction. CoRR (2021)
Denis Belomestny, Ilya Levin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Veronika Zorina
Model-free policy evaluation in Reinforcement Learning via upper solutions. CoRR (2021)
Gersende Fort, Eric Moulines, Hoi-To Wai
Geom-Spider-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization. ICASSP (2021)
Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov
Monte Carlo Variational Auto-Encoders. ICML (2021)
Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize. CoRR (2021)
Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning. CoRR (2021)
Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Hoi-To Wai
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning. COLT (2021)
Gersende Fort, Eric Moulines
The Perturbed Prox-Preconditioned Spider Algorithm for EM-Based Large Scale Learning. SSP (2021)
Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Geneviève Robin
Federated-EM with heterogeneity mitigation and variance reduction. NeurIPS (2021)
Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Hoi-To Wai
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning. CoRR (2021)
Gersende Fort, P. Gach, Eric Moulines
Fast incremental expectation maximization for finite-sum optimization: nonasymptotic convergence. Stat. Comput. 31 (4) (2021)
Achille Thin, Yazid Janati El Idrissi, Sylvain Le Corff, Charles Ollion, Eric Moulines, Arnaud Doucet, Alain Durmus, Christian X. Robert
NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform. NeurIPS (2021)
Nicolas Brosse, Carlos Riquelme, Alice Martin, Sylvain Gelly, Eric Moulines
On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation. CoRR (2020)
Maxim Kaledin, Eric Moulines, Alexey Naumov, Vladislav Tadic, Hoi-To Wai
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise. CoRR (2020)
Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov
Variance reduction for Markov chains with application to MCMC. Stat. Comput. 30 (4) (2020)
Maxim Kaledin, Eric Moulines, Alexey Naumov, Vladislav Tadic, Hoi-To Wai
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise. COLT (2020)
Gersende Fort, Eric Moulines, Hoi-To Wai
Geom-SPIDER-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization. CoRR (2020)
Gersende Fort, Eric Moulines, Hoi-To Wai
A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm. CoRR (2020)
Toni Karvonen, Silvère Bonnabel, Eric Moulines, Simo Särkkä
On Stability of a Class of Filters for Nonlinear Stochastic Systems. SIAM J. Control. Optim. 58 (4) (2020)
Achille Thin, Nikita Kotelevskii, Jean-Stanislas Denain, Leo Grinsztajn, Alain Durmus, Maxim Panov, Eric Moulines
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference. CoRR (2020)
Robert Mattila, Cristian R. Rojas, Eric Moulines, Vikram Krishnamurthy, Bo Wahlberg
Fast and Consistent Learning of Hidden Markov Models by Incorporating Non-Consecutive Correlations. ICML (2020)
Belhal Karimi, Marc Lavielle, Eric Moulines
f-SAEM: A fast stochastic approximation of the EM algorithm for nonlinear mixed effects models. Comput. Stat. Data Anal. 141 (2020)
Gersende Fort, Eric Moulines, Hoi-To Wai
A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm. NeurIPS (2020)
Gersende Fort, P. Gach, Eric Moulines
Fast Incremental Expectation Maximization for finite-sum optimization: nonasymptotic convergence. CoRR (2020)
Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov
Variance reduction for Markov chains with application to MCMC. CoRR (2019)
Belhal Karimi, Hoi-To Wai, Eric Moulines, Marc Lavielle
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods. NeurIPS (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Eric Moulines