Ring-LWE in Polynomial Rings. - Léo Ducas; Alain Durmus (IACR Cryptol. ePrint Arch., 2012)

Lattice Signatures and Bimodal Gaussians. - Léo Ducas; Alain Durmus; Tancrède Lepoint; Vadim Lyubashevsky (IACR Cryptol. ePrint Arch., 2013)

Quantitative bounds of convergence for geometrically ergodic Markov chain in the Wasserstein distance with application to the Metropolis Adjusted Langevin Algorithm. - Alain Durmus; Eric Moulines (Stat. Comput., 2015)

Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo. - Alain Durmus; Umut Simsekli; Eric Moulines; Roland Badeau; Gaël Richard (NIPS, 2016)

Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo. - Nicolas Brosse; Alain Durmus; Eric Moulines; Marcelo Pereyra (COLT, 2017)

Parallelized Stochastic Gradient Markov Chain Monte Carlo algorithms for non-negative matrix factorization. - Umut Simsekli; Alain Durmus; Roland Badeau; Gaël Richard; Eric Moulines; A. Taylan Cemgil (ICASSP, 2017)

Optimal scaling of the random walk Metropolis algorithm under L p mean differentiability. - Alain Durmus; Sylvain Le Corff; Eric Moulines; Gareth O. Roberts (J. Appl. Probab., 2017)

The promises and pitfalls of Stochastic Gradient Langevin Dynamics. - Nicolas Brosse; Alain Durmus; Eric Moulines (NeurIPS, 2018)

Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions. - Umut Simsekli; Antoine Liutkus; Szymon Majewski; Alain Durmus (CoRR, 2018)

Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau. - Alain Durmus; Eric Moulines; Marcelo Pereyra (SIAM J. Imaging Sci., 2018)

Markov Decision Process for MOOC Users Behavioral Inference. - Firas Jarboui; Célya Gruson-Daniel; Alain Durmus; Vincent Rocchisani; Sophie-Helene Goulet Ebongue; Anneliese Depoux; Wilfried Kirschenmann; Vianney Perchet (EMOOCs, 2019)

Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions. - Antoine Liutkus; Umut Simsekli; Szymon Majewski; Alain Durmus; Fabian-Robert Stöter (ICML, 2019)

Copula-like Variational Inference. - Marcel Hirt; Petros Dellaportas; Alain Durmus (NeurIPS, 2019)

Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance. - Kimia Nadjahi; Alain Durmus; Umut Simsekli; Roland Badeau (NeurIPS, 2019)

Analysis of Langevin Monte Carlo via Convex Optimization. - Alain Durmus; Szymon Majewski; Blazej Miasojedow (J. Mach. Learn. Res., 2019)

Approximate Bayesian Computation with the Sliced-Wasserstein Distance. - Kimia Nadjahi; Valentin De Bortoli; Alain Durmus; Roland Badeau; Umut Simsekli (ICASSP, 2020)

Quantitative Propagation of Chaos for SGD in Wide Neural Networks. - Valentin De Bortoli; Alain Durmus; Xavier Fontaine; Umut Simsekli (NeurIPS, 2020)

Statistical and Topological Properties of Sliced Probability Divergences. - Kimia Nadjahi; Alain Durmus; Lénaïc Chizat; Soheil Kolouri; Shahin Shahrampour; Umut Simsekli (NeurIPS, 2020)

MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference. - Achille Thin; Nikita Kotelevskii; Jean-Stanislas Denain; Léo Grinsztajn; Alain Durmus; Maxim Panov; Eric Moulines (CoRR, 2020)

Convergence Analysis of Riemannian Stochastic Approximation Schemes. - Alain Durmus; Pablo Jiménez; Éric Moulines; Salem Said; Hoi-To Wai (CoRR, 2020)

Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach. Part II: Theoretical Analysis. - Valentin De Bortoli; Alain Durmus; Marcelo Pereyra; Ana Fernandez Vidal (SIAM J. Imaging Sci., 2020)

Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part I: Methodology and Experiments. - Ana Fernandez Vidal; Valentin De Bortoli; Marcelo Pereyra; Alain Durmus (SIAM J. Imaging Sci., 2020)

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

Convergence rates and approximation results for SGD and its continuous-time counterpart. - Xavier Fontaine; Valentin De Bortoli; Alain Durmus (COLT, 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 (ICML, 2021)

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

Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections. - Kimia Nadjahi; Alain Durmus; Pierre E. Jacob; Roland Badeau; Umut Simsekli (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 (NeurIPS, 2021)

Fast Bayesian Model Selection in Imaging Inverse Problems Using Residuals. - Ana Fernandez Vidal; Marcelo Pereyra; Alain Durmus; Jean-François Giovannelli (SSP, 2021)

DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm. - Vincent Plassier; Maxime Vono; Alain Durmus; Eric Moulines (CoRR, 2021)

Uniform minorization condition and convergence bounds for discretizations of kinetic Langevin dynamics. - Alain Durmus; Aurélien Enfroy; Éric Moulines; Gabriel Stoltz (CoRR, 2021)

Asymptotic bias of inexact Markov Chain Monte Carlo methods in high dimension. - Alain Durmus; Andreas Eberle (CoRR, 2021)

Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. - Valentin De Bortoli; Alain Durmus; Marcelo Pereyra; Ana Fernandez Vidal (Stat. Comput., 2021)

Maximum Entropy Methods for Texture Synthesis: Theory and Practice. - Valentin De Bortoli; Agnès Desolneux; Alain Durmus; Bruno Galerne; Arthur Leclaire (SIAM J. Math. Data Sci., 2021)

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

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

Local-Global MCMC kernels: the best of both worlds. - Sergey Samsonov; Evgeny Lagutin; Marylou Gabrié; Alain Durmus; Alexey Naumov; Eric Moulines (NeurIPS, 2022)

On the geometric convergence for MALA under verifiable conditions. - Alain Durmus; Éric Moulines (CoRR, 2022)

Boost your favorite Markov Chain Monte Carlo sampler using Kac's theorem: the Kick-Kac teleportation algorithm. - Randal Douc; Alain Durmus; Aurélien Enfroy; Jimmy Olsson (CoRR, 2022)

Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study. - Tom Huix; Szymon Majewski; Alain Durmus; Eric Moulines; Anna Korba (CoRR, 2022)

Barrier Hamiltonian Monte Carlo. - Maxence Noble; Valentin De Bortoli; Alain Durmus (CoRR, 2022)

Bayesian Imaging Using Plug & Play Priors: When Langevin Meets Tweedie. - Rémi Laumont; Valentin De Bortoli; Andrés Almansa; Julie Delon; Alain Durmus; Marcelo Pereyra (SIAM J. Imaging Sci., 2022)

A Proximal Markov Chain Monte Carlo Method for Bayesian Inference in Imaging Inverse Problems: When Langevin Meets Moreau. - Alain Durmus; Éric Moulines; Marcelo Pereyra (SIAM Rev., 2022)

Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo. - Tom Huix; Matthew Zhang; Alain Durmus (AISTATS, 2023)

Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. - Vincent Plassier; Eric Moulines; Alain Durmus (AISTATS, 2023)

Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach. - Giacomo Greco; Maxence Noble; Giovanni Conforti; Alain Durmus (COLT, 2023)

On Sampling with Approximate Transport Maps. - Louis Grenioux; Alain Oliviero Durmus; Eric Moulines; Marylou Gabrié (ICML, 2023)

Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent. - Kruno Lehman; Alain Durmus; Umut Simsekli (NeurIPS, 2023)

Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo. - Maxence Noble; Valentin De Bortoli; Alain Durmus (NeurIPS, 2023)

Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters. - Maxence Noble; Valentin De Bortoli; Arnaud Doucet; Alain Durmus (NeurIPS, 2023)

Second order quantitative bounds for unadjusted generalized Hamiltonian Monte Carlo. - Evan Camrud; Alain Oliviero Durmus; Pierre Monmarché; Gabriel Stoltz (CoRR, 2023)

VITS : Variational Inference Thomson Sampling for contextual bandits. - Pierre Clavier; Tom Huix; Alain Durmus (CoRR, 2023)

Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent. - Krunoslav Lehman Pavasovic; Alain Durmus; Umut Simsekli (CoRR, 2023)

On Maximum a Posteriori Estimation with Plug & Play Priors and Stochastic Gradient Descent. - Rémi Laumont; Valentin De Bortoli; Andrés Almansa; Julie Delon; Alain Durmus; Marcelo Pereyra (J. Math. Imaging Vis., 2023)

Stochastic Approximation with Biased MCMC for Expectation Maximization. - Samuel Gruffaz; Kyurae Kim; Alain Durmus; Jacob R. Gardner (AISTATS, 2024)

Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training. - Tom Sander; Maxime Sylvestre; Alain Durmus (AISTATS, 2024)

VITS : Variational Inference Thompson Sampling for contextual bandits. - Pierre Clavier; Tom Huix; Alain Oliviero Durmus (ICML, 2024)

Stochastic Localization via Iterative Posterior Sampling. - Louis Grenioux; Maxence Noble; Marylou Gabrié; Alain Oliviero Durmus (ICML, 2024)

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

Differentially Private Representation Learning via Image Captioning. - Tom Sander; Yaodong Yu; Maziar Sanjabi; Alain Oliviero Durmus; Yi Ma 001; Kamalika Chaudhuri; Chuan Guo 001 (ICML, 2024)

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

Piecewise deterministic generative models. - Andrea Bertazzi; Dario Shariatian; Umut Simsekli; Eric Moulines; Alain Durmus (NeurIPS, 2024)

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

Shape analysis for time series. - Thibaut Germain; Samuel Gruffaz; Charles Truong; Alain Durmus; Laurent Oudre (NeurIPS, 2024)

Divide-and-Conquer Posterior Sampling for Denoising Diffusion priors. - Yazid Janati; Badr Moufad; Alain Durmus; Eric Moulines; Jimmy Olsson (NeurIPS, 2024)

Watermarking Makes Language Models Radioactive. - Tom Sander; Pierre Fernandez; Alain Durmus; Matthijs Douze; Teddy Furon (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)

Theoretical guarantees in KL for Diffusion Flow Matching. - Marta Gentiloni Silveri; Alain Durmus; Giovanni Conforti (NeurIPS, 2024)

Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors. - Yazid Janati El Idrissi; Alain Durmus; Eric Moulines; Jimmy Olsson (CoRR, 2024)

Mitigating Externalities while Learning: an Online Version of the Coase Theorem. - Antoine Scheid; Aymeric Capitaine; Etienne Boursier; Eric Moulines; Michael I. Jordan; Alain Durmus (CoRR, 2024)

Denoising Lévy Probabilistic Models. - Dario Shariatian; Umut Simsekli; Alain Durmus (CoRR, 2024)

Optimal Design for Reward Modeling in RLHF. - Antoine Scheid; Etienne Boursier; Alain Durmus; Michael I. Jordan; Pierre Ménard; Eric Moulines; Michal Valko (CoRR, 2024)

Learned Reference-based Diffusion Sampling for multi-modal distributions. - Maxence Noble; Louis Grenioux; Marylou Gabrié; Alain Oliviero Durmus (CoRR, 2024)

Joint Channel Selection using FedDRL in V2X. - Lorenzo Mancini; Safwan Labbi; Karim Abed-Meraim; Fouzi Boukhalfa; Alain Durmus; Paul Mangold; Eric Moulines (CoRR, 2024)

Refined Analysis of Federated Averaging's Bias and Federated Richardson-Romberg Extrapolation. - Paul Mangold; Alain Durmus; Aymeric Dieuleveut; Sergey Samsonov; Eric Moulines (CoRR, 2024)

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

Personalized Convolutional Dictionary Learning of Physiological Time Series. - Axel Roques; Samuel Gruffaz; Kyurae Kim; Alain Oliviero Durmus; Laurent Oudre (AISTATS, 2025)

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

Learned Reference-based Diffusion Sampler for multi-modal distributions. - Maxence Noble; Louis Grenioux; Marylou Gabrié; Alain Oliviero Durmus (ICLR, 2025)

Watermark Anything With Localized Messages. - Tom Sander; Pierre Fernandez; Alain Oliviero Durmus; Teddy Furon; Matthijs Douze (ICLR, 2025)

Heavy-Tailed Diffusion with Denoising Levy Probabilistic Models. - Dario Shariatian; Umut Simsekli; Alain Oliviero Durmus (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 (ICLR, 2025)

Finite-Time High-Probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation. - Alain Durmus; Eric Moulines; Alexey Naumov; Sergey Samsonov (Math. Oper. Res., 2025)

KL Convergence Guarantees for Score Diffusion Models under Minimal Data Assumptions. - Giovanni Conforti; Alain Durmus; Marta Gentiloni Silveri (SIAM J. Math. Data Sci., 2025)

Prediction-Aware Learning in Multi-Agent Systems. - Aymeric Capitaine; Etienne Boursier; Eric Moulines; Michael I. Jordan; Alain Durmus (CoRR, 2025)

Learning Contracts in Hierarchical Multi-Agent Systems. - Antoine Scheid; Etienne Boursier; Alain Durmus; Eric Moulines; Michael I. Jordan (CoRR, 2025)

A Mixture-Based Framework for Guiding Diffusion Models. - Yazid Janati; Badr Moufad; Mehdi Abou El Qassime; Alain Durmus; Eric Moulines; Jimmy Olsson (CoRR, 2025)

Discrete Markov Probabilistic Models. - Le-Tuyet-Nhi Pham; Dario Shariatian; Antonio Ocello; Giovanni Conforti; Alain Durmus (CoRR, 2025)

Differential privacy guarantees of Markov chain Monte Carlo algorithms. - Andrea Bertazzi; Tim Johnston; Gareth O. Roberts; Alain Durmus (CoRR, 2025)

Detecting Benchmark Contamination Through Watermarking. - Tom Sander; Pierre Fernandez; Saeed Mahloujifar; Alain Durmus; Chuan Guo 001 (CoRR, 2025)

Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up. - Paul Mangold; Alain Durmus; Aymeric Dieuleveut; Eric Moulines (CoRR, 2025)

Online Decision-Focused Learning. - Aymeric Capitaine; Maxime Haddouche; Eric Moulines; Michael I. Jordan; Etienne Boursier; Alain Durmus (CoRR, 2025)

Conditional Diffusion Models with Classifier-Free Gibbs-like Guidance. - Badr Moufad; Yazid Janati; Alain Durmus; Ahmed Ghorbel; Eric Moulines; Jimmy Olsson (CoRR, 2025)

Algorithm- and Data-Dependent Generalization Bounds for Score-Based Generative Models. - Benjamin Dupuis; Dario Shariatian; Maxime Haddouche; Alain Durmus; Umut Simsekli (CoRR, 2025)