Alain Durmus

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 73

Top Topics

Stochastic Approximation

Markov Chain Monte Carlo

Maximum Likelihood Estimation

Variational Inference

Top Venues

SIAM J. Imaging Sci.

Publications

Tom Sander, Maxime Sylvestre, Alain Durmus
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training. CoRR (2024)
Vincent Plassier, Eric Moulines, Alain Durmus
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. AISTATS (2023)
Louis Grenioux, Alain Durmus, Éric Moulines, Marylou Gabrié
On Sampling with Approximate Transport Maps. CoRR (2023)
Tom Huix, Matthew Zhang, Alain Durmus
Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo. AISTATS (2023)
Krunoslav Lehman Pavasovic, Alain Durmus, Umut Simsekli
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent. CoRR (2023)
Kruno Lehman, Alain Durmus, Umut Simsekli
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent. NeurIPS (2023)
Pierre Clavier, Tom Huix, Alain Durmus
VITS : Variational Inference Thomson Sampling for contextual bandits. CoRR (2023)
Giacomo Greco, Maxence Noble, Giovanni Conforti, Alain Durmus
Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach. COLT (2023)
Maxence Noble, Valentin De Bortoli, Arnaud Doucet, Alain Durmus
Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters. CoRR (2023)
Maxence Noble, Valentin De Bortoli, Alain Durmus
Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo. NeurIPS (2023)
Rémi Laumont, Valentin De Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra
On Maximum a Posteriori Estimation with Plug & Play Priors and Stochastic Gradient Descent. J. Math. Imaging Vis. 65 (1) (2023)
Maxence Noble, Valentin De Bortoli, Arnaud Doucet, Alain Durmus
Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters. 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)
Alain Durmus, Éric Moulines
On the geometric convergence for MALA under verifiable conditions. CoRR (2022)
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) (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)
Rémi Laumont, Valentin De Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra
Bayesian Imaging Using Plug & Play Priors: When Langevin Meets Tweedie. SIAM J. Imaging Sci. 15 (2) (2022)
Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines
Local-Global MCMC kernels: the best of both worlds. NeurIPS (2022)
Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. AISTATS (2022)
Randal Douc, Alain Durmus, Aurélien Enfroy, Jimmy Olsson
Boost your favorite Markov Chain Monte Carlo sampler using Kac's theorem: the Kick-Kac teleportation algorithm. CoRR (2022)
Rémi Laumont, Valentin De Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra
On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent. CoRR (2022)
Nikita Kotelevskii, Maxime Vono, Alain Durmus, Eric Moulines
FedPop: A Bayesian Approach for Personalised Federated Learning. NeurIPS (2022)
Maxence Noble, Valentin De Bortoli, Alain Durmus
Barrier Hamiltonian Monte Carlo. CoRR (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)
Xavier Fontaine, Valentin De Bortoli, Alain Durmus
Convergence rates and approximation results for SGD and its continuous-time counterpart. COLT (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. NeurIPS (2021)
Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm. CoRR (2021)
Alain Durmus, Pablo Jiménez, Eric Moulines, Salem Said
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size. AISTATS (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)
Rémi Laumont, Valentin De Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie. CoRR (2021)
Alain Durmus, Aurélien Enfroy, Éric Moulines, Gabriel Stoltz
Uniform minorization condition and convergence bounds for discretizations of kinetic Langevin dynamics. CoRR (2021)
Alain Durmus, Andreas Eberle
Asymptotic bias of inexact Markov Chain Monte Carlo methods in high dimension. CoRR (2021)
Ana Fernandez Vidal, Marcelo Pereyra, Alain Durmus, Jean-François Giovannelli
Fast Bayesian Model Selection in Imaging Inverse Problems Using Residuals. SSP (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)
Alain Durmus, Pablo Jiménez, Éric Moulines, Salem Said
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size. 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)
Kimia Nadjahi, Alain Durmus, Pierre E. Jacob, Roland Badeau, Umut Simsekli
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections. 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)
Valentin De Bortoli, Agnès Desolneux, Alain Durmus, Bruno Galerne, Arthur Leclaire
Maximum Entropy Methods for Texture Synthesis: Theory and Practice. SIAM J. Math. Data Sci. 3 (1) (2021)
Valentin De Bortoli, Alain Durmus, Marcelo Pereyra, Ana Fernandez Vidal
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Stat. Comput. 31 (3) (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)
Kimia Nadjahi, Alain Durmus, Pierre E. Jacob, Roland Badeau, Umut Simsekli
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections. CoRR (2021)
Kimia Nadjahi, Valentin De Bortoli, Alain Durmus, Roland Badeau, Umut Simsekli
Approximate Bayesian Computation with the Sliced-Wasserstein Distance. ICASSP (2020)
Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Simsekli
Statistical and Topological Properties of Sliced Probability Divergences. NeurIPS (2020)
Ana Fernandez Vidal, Valentin De Bortoli, Marcelo Pereyra, Alain Durmus
Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part I: Methodology and Experiments. SIAM J. Imaging Sci. 13 (4) (2020)
Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli
Quantitative Propagation of Chaos for SGD in Wide Neural Networks. CoRR (2020)
Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Simsekli
Statistical and Topological Properties of Sliced Probability Divergences. CoRR (2020)
Alain Durmus, Pablo Jiménez, Éric Moulines, Salem Said, Hoi-To Wai
Convergence Analysis of Riemannian Stochastic Approximation Schemes. CoRR (2020)
Valentin De Bortoli, Alain Durmus, Marcelo Pereyra, Ana Fernandez Vidal
Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach. Part II: Theoretical Analysis. SIAM J. Imaging Sci. 13 (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)
Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli
Quantitative Propagation of Chaos for SGD in Wide Neural Networks. NeurIPS (2020)
Firas Jarboui, Célya Gruson-Daniel, Alain Durmus, Vincent Rocchisani, Sophie-Helene Goulet Ebongue, Anneliese Depoux, Wilfried Kirschenmann, Vianney Perchet
Markov Decision Process for MOOC users behavioral inference. CoRR (2019)
Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, Fabian-Robert Stöter
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions. ICML (2019)
Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance. CoRR (2019)
Marcel Hirt, Petros Dellaportas, Alain Durmus
Copula-like Variational Inference. CoRR (2019)
Alain Durmus, Szymon Majewski, Blazej Miasojedow
Analysis of Langevin Monte Carlo via Convex Optimization. J. Mach. Learn. Res. 20 (2019)
Valentin De Bortoli, Agnès Desolneux, Alain Durmus, Bruno Galerne, Arthur Leclaire
Maximum entropy methods for texture synthesis: theory and practice. CoRR (2019)
Marcel Hirt, Petros Dellaportas, Alain Durmus
Copula-like Variational Inference. NeurIPS (2019)
Firas Jarboui, Célya Gruson-Daniel, Alain Durmus, Vincent Rocchisani, Sophie-Helene Goulet Ebongue, Anneliese Depoux, Wilfried Kirschenmann, Vianney Perchet
Markov Decision Process for MOOC Users Behavioral Inference. EMOOCs (2019)
Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance. NeurIPS (2019)
Alain Durmus, Eric Moulines, Marcelo Pereyra
Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau. SIAM J. Imaging Sci. 11 (1) (2018)
Nicolas Brosse, Alain Durmus, Eric Moulines
The promises and pitfalls of Stochastic Gradient Langevin Dynamics. CoRR (2018)
Umut Simsekli, Antoine Liutkus, Szymon Majewski, Alain Durmus
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions. CoRR (2018)
Nicolas Brosse, Alain Durmus, Eric Moulines
The promises and pitfalls of Stochastic Gradient Langevin Dynamics. NeurIPS (2018)
Umut Simsekli, Alain Durmus, Roland Badeau, Gaël Richard, Eric Moulines, A. Taylan Cemgil
Parallelized Stochastic Gradient Markov Chain Monte Carlo algorithms for non-negative matrix factorization. ICASSP (2017)
Alain Durmus, Sylvain Le Corff, Eric Moulines, Gareth O. Roberts
Optimal scaling of the random walk Metropolis algorithm under L p mean differentiability. J. Appl. Probab. 54 (4) (2017)
Nicolas Brosse, Alain Durmus, Eric Moulines, Marcelo Pereyra
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo. COLT (2017)
Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël Richard
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo. NIPS (2016)
Alain Durmus, Eric Moulines
Quantitative bounds of convergence for geometrically ergodic Markov chain in the Wasserstein distance with application to the Metropolis Adjusted Langevin Algorithm. Stat. Comput. 25 (1) (2015)
Léo Ducas, Alain Durmus, Tancrède Lepoint, Vadim Lyubashevsky
Lattice Signatures and Bimodal Gaussians. CRYPTO (1) (2013)
Léo Ducas, Alain Durmus, Tancrède Lepoint, Vadim Lyubashevsky
Lattice Signatures and Bimodal Gaussians. IACR Cryptol. ePrint Arch. 2013 (2013)
Léo Ducas, Alain Durmus
Ring-LWE in Polynomial Rings. IACR Cryptol. ePrint Arch. 2012 (2012)
Léo Ducas, Alain Durmus
Ring-LWE in Polynomial Rings. Public Key Cryptography (2012)