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Eric Moulines
Éric Moulines
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2020 – today
- 2024
- [j76]Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov:
Rates of convergence for density estimation with generative adversarial networks. J. Mach. Learn. Res. 25: 29:1-29:47 (2024) - [c142]Louis Leconte, Matthieu Jonckheere, Sergey Samsonov, Eric Moulines:
Queuing dynamics of asynchronous Federated Learning. AISTATS 2024: 1711-1719 - [c141]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: 4879-4887 - [c140]Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines:
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability. COLT 2024: 4511-4547 - [c139]Louis Leconte, Van Minh Nguyen, Eric Moulines:
FAVANO: Federated Averaging with Asynchronous Nodes. ICASSP 2024: 5665-5669 - [c138]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: 1956-1961 - [c137]Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines:
Monte Carlo guided Denoising Diffusion models for Bayesian linear inverse problems. ICLR 2024 - [c136]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Demonstration-Regularized RL. ICLR 2024 - [c135]Tom Huix, Anna Korba, Alain Oliviero Durmus, Eric Moulines:
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians. ICML 2024 - [c134]Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Oliviero Durmus:
Incentivized Learning in Principal-Agent Bandit Games. ICML 2024 - [c133]Jade Eva Guisiano, Domenico Barretta, Éric Moulines, Thomas Lauvaux, Jérémie Sublime:
Object Detection Models Sensitivity & Robustness to Satellite-based Adversarial Attacks. IGARSS 2024: 7844-7848 - [c132]Lisa Bedin, Gabriel Cardoso, Josselin Duchateau, Rémi Dubois, Eric Moulines:
Leveraging an ECG Beat Diffusion Model for Morphological Reconstruction from Indirect Signals. NeurIPS 2024 - [c131]Andrea Bertazzi, Dario Shariatian, Umut Simsekli, Eric Moulines, Alain Durmus:
Piecewise deterministic generative models. NeurIPS 2024 - [c130]Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus:
Unravelling in Collaborative Learning. NeurIPS 2024 - [c129]Yazid Janati, Badr Moufad, Alain Durmus, Eric Moulines, Jimmy Olsson:
Divide-and-Conquer Posterior Sampling for Denoising Diffusion priors. NeurIPS 2024 - [c128]Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Réda Alami, Alexey Naumov, Eric Moulines:
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning. NeurIPS 2024 - [c127]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. NeurIPS 2024 - [c126]Antoine Scheid, Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus:
Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality. NeurIPS 2024 - [i79]Gabriel Victorino Cardoso, Lisa Bedin, Josselin Duchateau, Rémi Dubois, Eric Moulines:
Bayesian ECG reconstruction using denoising diffusion generative models. CoRR abs/2401.05388 (2024) - [i78]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 abs/2402.04114 (2024) - [i77]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 abs/2403.03811 (2024) - [i76]Yazid Janati El Idrissi, Alain Durmus, Eric Moulines, Jimmy Olsson:
Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors. CoRR abs/2403.11407 (2024) - [i75]Louis Leconte, Matthieu Jonckheere, Sergey Samsonov, Eric Moulines:
Queuing dynamics of asynchronous Federated Learning. CoRR abs/2405.00017 (2024) - [i74]Louis Leconte, Lisa Bedin, Van Minh Nguyen, Eric Moulines:
ReALLM: A general framework for LLM compression and fine-tuning. CoRR abs/2405.13155 (2024) - [i73]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 abs/2405.16644 (2024) - [i72]Tom Huix, Anna Korba, Alain Durmus, Eric Moulines:
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians. CoRR abs/2406.04012 (2024) - [i71]Arnaud Descours, Tom Huix, Arnaud Guillin, Manon Michel, Éric Moulines, Boris Nectoux:
Central Limit Theorem for Bayesian Neural Network trained with Variational Inference. CoRR abs/2406.09048 (2024) - [i70]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 abs/2406.19824 (2024) - [i69]Vincent Plassier, Alexander Fishkov, Maxim Panov, Eric Moulines:
Conditionally valid Probabilistic Conformal Prediction. CoRR abs/2407.01794 (2024) - [i68]Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus:
Unravelling in Collaborative Learning. CoRR abs/2407.14332 (2024) - [i67]Andrea Bertazzi, Alain Oliviero Durmus, Dario Shariatian, Umut Simsekli, Eric Moulines:
Piecewise deterministic generative models. CoRR abs/2407.19448 (2024) - [i66]Guokan Shang, Hadi Abdine, Yousef Khoubrane, Amr Mohamed, Yassine Abbahaddou, Sofiane Ennadir, Imane Momayiz, Xuguang Ren, Eric Moulines, Preslav Nakov, Michalis Vazirgiannis, Eric P. Xing:
Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect. CoRR abs/2409.17912 (2024) - [i65]Pierre Perrault, Denis Belomestny, Pierre Ménard, Éric Moulines, Alexey Naumov, Daniil Tiapkin, Michal Valko:
A New Bound on the Cumulant Generating Function of Dirichlet Processes. CoRR abs/2409.18621 (2024) - [i64]Marina Sheshukova, Denis Belomestny, Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov:
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation. CoRR abs/2410.05106 (2024) - [i63]Badr Moufad, Yazid Janati, Lisa Bedin, Alain Durmus, Randal Douc, Eric Moulines, Jimmy Olsson:
Variational Diffusion Posterior Sampling with Midpoint Guidance. CoRR abs/2410.09945 (2024) - [i62]Antoine Scheid, Etienne Boursier, Alain Durmus, Michael I. Jordan, Pierre Ménard, Eric Moulines, Michal Valko:
Optimal Design for Reward Modeling in RLHF. CoRR abs/2410.17055 (2024) - [i61]Lorenzo Mancini, Safwan Labbi, Karim Abed-Meraim, Fouzi Boukhalfa, Alain Durmus, Paul Mangold, Eric Moulines:
Joint Channel Selection using FedDRL in V2X. CoRR abs/2410.20687 (2024) - [i60]Safwan Labbi, Daniil Tiapkin, Lorenzo Mancini, Paul Mangold, Eric Moulines:
Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents. CoRR abs/2410.22908 (2024) - [i59]Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines:
Refined Analysis of Federated Averaging's Bias and Federated Richardson-Romberg Extrapolation. CoRR abs/2412.01389 (2024) - 2023
- [j75]Anatoli B. Juditsky, Joon Kwon
, Éric Moulines:
Unifying mirror descent and dual averaging. Math. Program. 199(1): 793-830 (2023) - [j74]Gersende Fort, Eric Moulines:
Stochastic variable metric proximal gradient with variance reduction for non-convex composite optimization. Stat. Comput. 33(3): 65 (2023) - [j73]Mastane Achab, Réda Alami, Yasser Abdelaziz Dahou Djilali, Kirill Fedyanin, Eric Moulines:
One-Step Distributional Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023) - [j72]Aymeric Dieuleveut
, Gersende Fort
, Eric Moulines
, Hoi-To Wai
:
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning. IEEE Trans. Signal Process. 71: 3117-3148 (2023) - [c125]Louis Leconte, Sholom Schechtman, Eric Moulines:
ASkewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks. AISTATS 2023: 3644-3663 - [c124]Vincent Plassier, Eric Moulines, Alain Durmus:
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. AISTATS 2023: 5299-5356 - [c123]Réda Alami, Mohammed Mahfoud, Eric Moulines:
Restarted Bayesian Online Change-point Detection for Non-Stationary Markov Decision Processes. CoLLAs 2023: 715-744 - [c122]Sholom Schechtman, Daniil Tiapkin
, Michael Muehlebach, Éric Moulines:
Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold. COLT 2023: 1228-1258 - [c121]Arnaud Descours, Tom Huix, Arnaud Guillin, Manon Michel, Éric Moulines, Boris Nectoux:
Law of Large Numbers for Bayesian two-layer Neural Network trained with Variational Inference. COLT 2023: 4657-4695 - [c120]Louis Leconte, Van Minh Nguyen, Eric Moulines:
Federated Boolean Neural Networks Learning. FMEC 2023: 247-253 - [c119]Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff
, Eric Moulines, Jimmy Olsson:
State and parameter learning with PARIS particle Gibbs. ICML 2023: 3625-3675 - [c118]Louis Grenioux, Alain Oliviero Durmus, Eric Moulines, Marylou Gabrié:
On Sampling with Approximate Transport Maps. ICML 2023: 11698-11733 - [c117]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: 24517-24531 - [c116]Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov:
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. ICML 2023: 27907-27947 - [c115]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: 34161-34221 - [c114]Jade Eva Guisiano, Éric Moulines, Thomas Lauvaux, Jérémie Sublime:
Oil and Gas Automatic Infrastructure Mapping: Leveraging High-Resolution Satellite Imagery Through Fine-Tuning of Object Detection Models. ICONIP (12) 2023: 442-458 - [c113]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 - [c112]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 - [i58]Gersende Fort, Eric Moulines:
Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization. CoRR abs/2301.00631 (2023) - [i57]Louis Grenioux, Alain Durmus, Éric Moulines, Marylou Gabrié:
On Sampling with Approximate Transport Maps. CoRR abs/2302.04763 (2023) - [i56]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 abs/2303.08059 (2023) - [i55]Réda Alami, Mohammed Mahfoud, Eric Moulines:
Restarted Bayesian Online Change-point Detection for Non-Stationary Markov Decision Processes. CoRR abs/2304.00232 (2023) - [i54]Mastane Achab, Réda Alami, Yasser Abdelaziz Dahou Djilali, Kirill Fedyanin, Eric Moulines:
One-Step Distributional Reinforcement Learning. CoRR abs/2304.14421 (2023) - [i53]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 abs/2305.15938 (2023) - [i52]Louis Leconte, Van Minh Nguyen, Eric Moulines:
FAVAS: Federated AVeraging with ASynchronous clients. CoRR abs/2305.16099 (2023) - [i51]Louis Grenioux, Éric Moulines, Marylou Gabrié:
Balanced Training of Energy-Based Models with Adaptive Flow Sampling. CoRR abs/2306.00684 (2023) - [i50]Vincent Plassier, Mehdi Makni, Aleksandr Rubashevskii, Eric Moulines, Maxim Panov:
Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. CoRR abs/2306.05131 (2023) - [i49]Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, Eric Moulines:
Monte Carlo guided Diffusion for Bayesian linear inverse problems. CoRR abs/2308.07983 (2023) - [i48]Fouzi Boukhalfa, Réda Alami, Mastane Achab, Eric Moulines, Mehdi Bennis:
Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study. CoRR abs/2310.03767 (2023) - [i47]Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines:
Finite-Sample Analysis of the Temporal Difference Learning. CoRR abs/2310.14286 (2023) - [i46]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Demonstration-Regularized RL. CoRR abs/2310.17303 (2023) - [i45]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 abs/2310.18186 (2023) - [i44]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 abs/2312.15799 (2023) - 2022
- [j71]Denis Belomestny, Eric Moulines, Sergey Samsonov
:
Variance reduction for additive functionals of Markov chains via martingale representations. Stat. Comput. 32(1): 16 (2022) - [j70]Alain Durmus, Éric Moulines, Marcelo Pereyra
:
A Proximal Markov Chain Monte Carlo Method for Bayesian Inference in Imaging Inverse Problems: When Langevin Meets Moreau. SIAM Rev. 64(4): 991-1028 (2022) - [c111]Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines:
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. AISTATS 2022: 6459-6500 - [c110]Belhal Karimi, Hoi-To Wai, Eric Moulines, Ping Li:
Minimization by Incremental Stochastic Surrogate Optimization for Large Scale Nonconvex Problems. ALT 2022: 606-637 - [c109]Gabriel Cardoso, Geneviève Robin, Andony Arrieula, Mark Potse, Michel Haïssaguerre
, Eric Moulines, Rémi Dubois:
A Patient-Specific Single Equivalent Dipole Model. CinC 2022: 1-4 - [c108]Max Cohen, Guillaume Quispe, Sylvain Le Corff
, Charles Ollion, Eric Moulines:
Diffusion bridges vector quantized variational autoencoders. ICML 2022: 4141-4156 - [c107]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: 21380-21431 - [c106]Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson:
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling. NeurIPS 2022 - [c105]Nikita Kotelevskii, Maxime Vono, Alain Durmus, Eric Moulines:
FedPop: A Bayesian Approach for Personalised Federated Learning. NeurIPS 2022 - [c104]Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines:
Local-Global MCMC kernels: the best of both worlds. NeurIPS 2022 - [c103]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 - [i43]Alain Durmus, Éric Moulines:
On the geometric convergence for MALA under verifiable conditions. CoRR abs/2201.01951 (2022) - [i42]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 abs/2205.07704 (2022) - [i41]Nikita Kotelevskii, Maxime Vono, Eric Moulines, Alain Durmus:
FedPop: A Bayesian Approach for Personalised Federated Learning. CoRR abs/2206.03611 (2022) - [i40]Tom Huix, Szymon Majewski, Alain Durmus, Eric Moulines, Anna Korba:
Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study. CoRR abs/2207.03859 (2022) - [i39]Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov:
Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation. CoRR abs/2207.04475 (2022) - [i38]Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson:
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling. CoRR abs/2207.06364 (2022) - [i37]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 abs/2209.14414 (2022) - [i36]Vincent Plassier, Alain Durmus, Eric Moulines:
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. CoRR abs/2211.00100 (2022) - [i35]Louis Leconte, Sholom Schechtman, Eric Moulines:
AskewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks. CoRR abs/2211.03741 (2022) - 2021
- [j69]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): 507-535 (2021) - [j68]Gersende Fort
, P. Gach, Eric Moulines:
Fast incremental expectation maximization for finite-sum optimization: nonasymptotic convergence. Stat. Comput. 31(4): 48 (2021) - [j67]Ngoc Huy Chau, Éric Moulines, Miklós Rásonyi
, Sotirios Sabanis
, Ying Zhang
:
On Stochastic Gradient Langevin Dynamics with Dependent Data Streams: The Fully Nonconvex Case. SIAM J. Math. Data Sci. 3(3): 959-986 (2021) - [c102]Alain Durmus, Pablo Jiménez, Eric Moulines, Salem Said:
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size. AISTATS 2021: 1018-1026 - [c101]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: 1711-1752 - [c100]Gersende Fort, Eric Moulines, Hoi-To Wai:
Geom-Spider-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization. ICASSP 2021: 3135-3139 - [c99]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: 7654-7664 - [c98]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: 8577-8587 - [c97]Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov:
Monte Carlo Variational Auto-Encoders. ICML 2021: 10247-10257 - [c96]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: 17060-17071 - [c95]Aymeric Dieuleveut, Gersende Fort, Eric Moulines, Geneviève Robin:
Federated-EM with heterogeneity mitigation and variance reduction. NeurIPS 2021: 29553-29566 - [c94]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: 30063-30074 - [c93]Gersende Fort, Eric Moulines:
The Perturbed Prox-Preconditioned Spider Algorithm: Non-Asymptotic Convergence Bounds. SSP 2021: 96-100 - [c92]Gersende Fort, Eric Moulines:
The Perturbed Prox-Preconditioned Spider Algorithm for EM-Based Large Scale Learning. SSP 2021: 316-320 - [i34]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 abs/2102.00185 (2021) - [i33]Alain Durmus, Pablo Jiménez, Éric Moulines, Salem Said:
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size. CoRR abs/2102.07586 (2021) - [i32]Denis Belomestny, Ilya Levin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Veronika Zorina:
Model-free policy evaluation in Reinforcement Learning via upper solutions. CoRR abs/2105.02135 (2021) - [i31]Gersende Fort, Eric Moulines:
The Perturbed Prox-Preconditioned SPIDER algorithm for EM-based large scale learning. CoRR abs/2105.11732 (2021) - [i30]Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines:
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning. CoRR abs/2106.00797 (2021) - [i29]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 abs/2106.01257 (2021) - [i28]Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines:
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm. CoRR abs/2106.06300 (2021) - [i27]Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov:
Monte Carlo Variational Auto-Encoders. CoRR abs/2106.15921 (2021) - [i26]Alain Durmus, Aurélien Enfroy, Éric Moulines, Gabriel Stoltz:
Uniform minorization condition and convergence bounds for discretizations of kinetic Langevin dynamics. CoRR abs/2107.14542 (2021) - [i25]Aymeric Dieuleveut
, Gersende Fort
, Eric Moulines, Geneviève Robin:
Federated Expectation Maximization with heterogeneity mitigation and variance reduction. CoRR abs/2111.02083 (2021) - [i24]Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines:
Ex2MCMC: Sampling through Exploration Exploitation. CoRR abs/2111.02702 (2021) - 2020
- [j66]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: 123-138 (2020) - [j65]