Durmus, Alain

35 publications

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

AISTATS 2024 Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training Tom Sander, Maxime Sylvestre, Alain 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 Piecewise Deterministic Generative Models Andrea Bertazzi, Dario Shariatian, Umut Simsekli, Eric Moulines, Alain Durmus

NeurIPS 2024 Shape Analysis for Time Series Thibaut Germain, Samuel Gruffaz, Charles Truong, Laurent Oudre, Alain Durmus

AISTATS 2024 Stochastic Approximation with Biased MCMC for Expectation Maximization Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob Gardner

NeurIPS 2024 Theoretical Guarantees in KL for Diffusion Flow Matching Marta Gentiloni Silveri, Giovanni Conforti, 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 Watermarking Makes Language Models Radioactive Tom Sander, Pierre Fernandez, Alain Durmus, Matthijs Douze, Teddy Furon

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

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

COLT 2023 Non-Asymptotic Convergence Bounds for Sinkhorn Iterates and Their Gradients: A Coupling Approach. Giacomo Greco, Maxence Noble, Giovanni Conforti, Alain Durmus

AISTATS 2023 Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo Tom Huix, Matthew Zhang, 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 Unbiased Constrained Sampling with Self-Concordant Barrier Hamiltonian Monte Carlo Maxence Noble, Valentin De Bortoli, Alain Durmus

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

NeurIPS 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

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

COLT 2021 Convergence Rates and Approximation Results for SGD and Its Continuous-Time Counterpart Xavier Fontaine, Valentin De Bortoli, Alain Durmus

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

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 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

JMLR 2019 Analysis of Langevin Monte Carlo via Convex Optimization Alain Durmus, Szymon Majewski, Błażej Miasojedow

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

NeurIPS 2019 Copula-like Variational Inference Marcel Hirt, Petros Dellaportas, Alain Durmus

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

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