Alain Durmus - RankMe

124 papers A* 27A 6Misc 2Journal 84Unranked 5

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Categorical Reparameterization with Denoising Diffusion models. CoRR Samson Gourevitch, Alain Durmus, Eric Moulines, Jimmy Olsson, Yazid Janati
2026	J	`jnl`	Dynamic Programming for Epistemic Uncertainty in Markov Decision Processes. CoRR Axel Benyamine, Julien Grand-Clément, Marek Petrik, Michael I. Jordan, Alain Durmus
2026	J	`jnl`	Sampling from multi-modal distributions on Riemannian manifolds with training-free stochastic interpolants. CoRR Alain Durmus, Maxence Noble, Thibaut Pellerin
2026	J	`jnl`	Test-then-Punish: A Statistical Approach to Repeated Games. CoRR Aymeric Capitaine, Antoine Scheid, Etienne Boursier, Alain Durmus, Michael I. Jordan
2025	J	`jnl`	A Mixture-Based Framework for Guiding Diffusion Models. CoRR Yazid Janati, Badr Moufad, Mehdi Abou El Qassime, Alain Durmus, Eric Moulines, Jimmy Olsson
2025	J	`jnl`	Algorithm- and Data-Dependent Generalization Bounds for Score-Based Generative Models. CoRR Benjamin Dupuis, Dario Shariatian, Maxime Haddouche, Alain Durmus, Umut Simsekli
2025	J	`jnl`	Briding Diffusion Posterior Sampling and Monte Carlo methods: a survey. CoRR Yazid Janati, Alain Durmus, Jimmy Olsson, Eric Moulines
2025	J	`jnl`	Conditional Diffusion Models with Classifier-Free Gibbs-like Guidance. CoRR Badr Moufad, Yazid Janati, Alain Durmus, Ahmed Ghorbel, Eric Moulines, Jimmy Olsson
2025	J	`jnl`	Detecting Benchmark Contamination Through Watermarking. CoRR Tom Sander, Pierre Fernandez, Saeed Mahloujifar, Alain Durmus, Chuan Guo
2025	J	`jnl`	Differential privacy guarantees of Markov chain Monte Carlo algorithms. CoRR Andrea Bertazzi, Tim Johnston, Gareth O. Roberts, Alain Durmus
2025	J	`jnl`	Discrete Markov Probabilistic Models. CoRR Le-Tuyet-Nhi Pham, Dario Shariatian, Antonio Ocello, Giovanni Conforti, Alain Durmus
2025	J	`jnl`	Exponential Convergence Guarantees for Iterative Markovian Fitting. CoRR Marta Gentiloni Silveri, Giovanni Conforti, Alain Durmus
2025	J	`jnl`	Finite-Time High-Probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation. Math. Oper. Res. Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov
2025	J	`jnl`	Iterative Tilting for Diffusion Fine-Tuning. CoRR Jean Pachebat, Giovanni Conforti, Alain Durmus, Yazid Janati
2025	J	`jnl`	KL Convergence Guarantees for Score Diffusion Models under Minimal Data Assumptions. SIAM J. Math. Data Sci. Giovanni Conforti, Alain Durmus, Marta Gentiloni Silveri
2025	J	`jnl`	Latent Discrete Diffusion Models. CoRR Dario Shariatian, Alain Durmus, Stefano Peluchetti
2025	J	`jnl`	Learning Contracts in Hierarchical Multi-Agent Systems. CoRR Antoine Scheid, Etienne Boursier, Alain Durmus, Eric Moulines, Michael I. Jordan
2025	J	`jnl`	Non-Asymptotic Analysis of Data Augmentation for Precision Matrix Estimation. CoRR Lucas Morisset, Adrien Hardy, Alain Durmus
2025	J	`jnl`	Non-Asymptotic Convergence of Discrete Diffusion Models: Masked and Random Walk dynamics. CoRR Giovanni Conforti, Alain Durmus, Le-Tuyet-Nhi Pham, Gaël Raoul
2025	J	`jnl`	On the Rate of Gaussian Approximation for Linear Regression Problems. CoRR Marat Khusainov, Marina Sheshukova, Alain Durmus, Sergey Samsonov
2025	J	`jnl`	Online Decision-Focused Learning. CoRR Aymeric Capitaine, Maxime Haddouche, Eric Moulines, Michael I. Jordan, Etienne Boursier, Alain Durmus
2025	J	`jnl`	Prediction-Aware Learning in Multi-Agent Systems. CoRR Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus
2025	J	`jnl`	Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up. CoRR Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Eric Moulines
2024	J	`jnl`	Denoising Lévy Probabilistic Models. CoRR Dario Shariatian, Umut Simsekli, Alain Durmus
2024	J	`jnl`	Differentially Private Representation Learning via Image Captioning. CoRR Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo
2024	J	`jnl`	Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors. CoRR Yazid Janati El Idrissi, Alain Durmus, Eric Moulines, Jimmy Olsson
2024	A*	`conf`	Divide-and-Conquer Posterior Sampling for Denoising Diffusion priors. NeurIPS Yazid Janati, Badr Moufad, Alain Durmus, Eric Moulines, Jimmy Olsson
2024	A	`conf`	Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training. AISTATS Tom Sander, Maxime Sylvestre, Alain Durmus
2024	J	`jnl`	Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training. CoRR Tom Sander, Maxime Sylvestre, Alain Durmus
2024	J	`jnl`	Incentivized Learning in Principal-Agent Bandit Games. CoRR Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, El Mahdi El Mhamdi, Eric Moulines, Michael I. Jordan, Alain Durmus
2024	J	`jnl`	Joint Channel Selection using FedDRL in V2X. CoRR Lorenzo Mancini, Safwan Labbi, Karim Abed-Meraim, Fouzi Boukhalfa, Alain Durmus, Paul Mangold, Eric Moulines
2024	A*	`conf`	Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationality. NeurIPS Antoine Scheid, Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus
2024	J	`jnl`	Mitigating Externalities while Learning: an Online Version of the Coase Theorem. CoRR Antoine Scheid, Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus
2024	J	`jnl`	Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation. CoRR Marina Sheshukova, Denis Belomestny, Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov
2024	J	`jnl`	Optimal Design for Reward Modeling in RLHF. CoRR Antoine Scheid, Etienne Boursier, Alain Durmus, Michael I. Jordan, Pierre Ménard, Eric Moulines, Michal Valko
2024	A*	`conf`	Piecewise deterministic generative models. NeurIPS Andrea Bertazzi, Dario Shariatian, Umut Simsekli, Eric Moulines, Alain Durmus
2024	J	`jnl`	Refined Analysis of Federated Averaging's Bias and Federated Richardson-Romberg Extrapolation. CoRR Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines
2024	A*	`conf`	Shape analysis for time series. NeurIPS Thibaut Germain, Samuel Gruffaz, Charles Truong, Alain Durmus, Laurent Oudre
2024	A	`conf`	Stochastic Approximation with Biased MCMC for Expectation Maximization. AISTATS Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob R. Gardner
2024	J	`jnl`	Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians. CoRR Tom Huix, Anna Korba, Alain Durmus, Eric Moulines
2024	A*	`conf`	Theoretical guarantees in KL for Diffusion Flow Matching. NeurIPS Marta Gentiloni Silveri, Alain Durmus, Giovanni Conforti
2024	J	`jnl`	Theoretical guarantees in KL for Diffusion Flow Matching. CoRR Marta Gentiloni Silveri, Giovanni Conforti, Alain Durmus
2024	A*	`conf`	Unravelling in Collaborative Learning. NeurIPS Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus
2024	J	`jnl`	Unravelling in Collaborative Learning. CoRR Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus
2024	J	`jnl`	Variational Diffusion Posterior Sampling with Midpoint Guidance. CoRR Badr Moufad, Yazid Janati, Lisa Bedin, Alain Durmus, Randal Douc, Eric Moulines, Jimmy Olsson
2024	J	`jnl`	Watermark Anything with Localized Messages. CoRR Tom Sander, Pierre Fernandez, Alain Durmus, Teddy Furon, Matthijs Douze
2024	A*	`conf`	Watermarking Makes Language Models Radioactive. NeurIPS Tom Sander, Pierre Fernandez, Alain Durmus, Matthijs Douze, Teddy Furon
2024	J	`jnl`	Watermarking Makes Language Models Radioactive. CoRR Tom Sander, Pierre Fernandez, Alain Durmus, Matthijs Douze, Teddy Furon
2023	A*	`conf`	Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent. NeurIPS Kruno Lehman, Alain Durmus, Umut Simsekli
2023	J	`jnl`	Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent. CoRR Krunoslav Lehman Pavasovic, Alain Durmus, Umut Simsekli
2023	A	`conf`	Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. AISTATS Vincent Plassier, Eric Moulines, Alain Durmus
2023	A*	`conf`	Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach. COLT Giacomo Greco, Maxence Noble, Giovanni Conforti, Alain Durmus
2023	J	`jnl`	On Maximum a Posteriori Estimation with Plug & Play Priors and Stochastic Gradient Descent. J. Math. Imaging Vis. Rémi Laumont, Valentin De Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra
2023	J	`jnl`	On Sampling with Approximate Transport Maps. CoRR Louis Grenioux, Alain Durmus, Éric Moulines, Marylou Gabrié
2023	A	`conf`	Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo. AISTATS Tom Huix, Matthew Zhang, Alain Durmus
2023	A*	`conf`	Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters. NeurIPS Maxence Noble, Valentin De Bortoli, Arnaud Doucet, Alain Durmus
2023	J	`jnl`	Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters. CoRR Maxence Noble, Valentin De Bortoli, Arnaud Doucet, Alain Durmus
2023	A*	`conf`	Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo. NeurIPS Maxence Noble, Valentin De Bortoli, Alain Durmus
2023	J	`jnl`	VITS : Variational Inference Thomson Sampling for contextual bandits. CoRR Pierre Clavier, Tom Huix, Alain Durmus
2022	J	`jnl`	A Proximal Markov Chain Monte Carlo Method for Bayesian Inference in Imaging Inverse Problems: When Langevin Meets Moreau. SIAM Rev. Alain Durmus, Éric Moulines, Marcelo Pereyra
2022	J	`jnl`	Barrier Hamiltonian Monte Carlo. CoRR Maxence Noble, Valentin De Bortoli, Alain Durmus
2022	J	`jnl`	Bayesian Imaging Using Plug & Play Priors: When Langevin Meets Tweedie. SIAM J. Imaging Sci. Rémi Laumont, Valentin De Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra
2022	J	`jnl`	Boost your favorite Markov Chain Monte Carlo sampler using Kac's theorem: the Kick-Kac teleportation algorithm. CoRR Randal Douc, Alain Durmus, Aurélien Enfroy, Jimmy Olsson
2022	A*	`conf`	FedPop: A Bayesian Approach for Personalised Federated Learning. NeurIPS Nikita Kotelevskii, Maxime Vono, Alain Durmus, Eric Moulines
2022	J	`jnl`	FedPop: A Bayesian Approach for Personalised Federated Learning. CoRR Nikita Kotelevskii, Maxime Vono, Eric Moulines, Alain Durmus
2022	J	`jnl`	Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. CoRR Vincent Plassier, Alain Durmus, Eric Moulines
2022	J	`jnl`	Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation. CoRR Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov
2022	A*	`conf`	Local-Global MCMC kernels: the best of both worlds. NeurIPS Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines
2022	J	`jnl`	On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent. CoRR Rémi Laumont, Valentin De Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra
2022	J	`jnl`	On the geometric convergence for MALA under verifiable conditions. CoRR Alain Durmus, Éric Moulines
2022	A	`conf`	QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. AISTATS Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines
2022	J	`jnl`	Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study. CoRR Tom Huix, Szymon Majewski, Alain Durmus, Eric Moulines, Anna Korba
2021	J	`jnl`	Asymptotic bias of inexact Markov Chain Monte Carlo methods in high dimension. CoRR Alain Durmus, Andreas Eberle
2021	J	`jnl`	Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie. CoRR Rémi Laumont, Valentin De Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra
2021	A*	`conf`	Convergence rates and approximation results for SGD and its continuous-time counterpart. COLT Xavier Fontaine, Valentin De Bortoli, Alain Durmus
2021	A*	`conf`	DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs. ICML Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines
2021	J	`jnl`	DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm. CoRR Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines
2021	J	`jnl`	Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Stat. Comput. Valentin De Bortoli, Alain Durmus, Marcelo Pereyra, Ana Fernandez Vidal
2021	A*	`conf`	Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections. NeurIPS Kimia Nadjahi, Alain Durmus, Pierre E. Jacob, Roland Badeau, Umut Simsekli
2021	J	`jnl`	Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections. CoRR Kimia Nadjahi, Alain Durmus, Pierre E. Jacob, Roland Badeau, Umut Simsekli
2021	—	`conf`	Fast Bayesian Model Selection in Imaging Inverse Problems Using Residuals. SSP Ana Fernandez Vidal, Marcelo Pereyra, Alain Durmus, Jean-François Giovannelli
2021	J	`jnl`	Maximum Entropy Methods for Texture Synthesis: Theory and Practice. SIAM J. Math. Data Sci. Valentin De Bortoli, Agnès Desolneux, Alain Durmus, Bruno Galerne, Arthur Leclaire
2021	A*	`conf`	Monte Carlo Variational Auto-Encoders. ICML Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov
2021	J	`jnl`	Monte Carlo Variational Auto-Encoders. CoRR Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov
2021	A*	`conf`	NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform. NeurIPS Achille Thin, Yazid Janati El Idrissi, Sylvain Le Corff, Charles Ollion, Eric Moulines, Arnaud Doucet, Alain Durmus, Christian X. Robert
2021	A	`conf`	On Riemannian Stochastic Approximation Schemes with Fixed Step-Size. AISTATS Alain Durmus, Pablo Jiménez, Eric Moulines, Salem Said
2021	J	`jnl`	On Riemannian Stochastic Approximation Schemes with Fixed Step-Size. CoRR Alain Durmus, Pablo Jiménez, Éric Moulines, Salem Said
2021	A*	`conf`	On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning. COLT Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Hoi-To Wai
2021	J	`jnl`	On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning. CoRR Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Hoi-To Wai
2021	J	`jnl`	QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning. CoRR Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines
2021	A*	`conf`	Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize. NeurIPS Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai
2021	J	`jnl`	Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize. CoRR Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai
2021	J	`jnl`	Uniform minorization condition and convergence bounds for discretizations of kinetic Langevin dynamics. CoRR Alain Durmus, Aurélien Enfroy, Éric Moulines, Gabriel Stoltz
2020	Misc	`conf`	Approximate Bayesian Computation with the Sliced-Wasserstein Distance. ICASSP Kimia Nadjahi, Valentin De Bortoli, Alain Durmus, Roland Badeau, Umut Simsekli
2020	J	`jnl`	Convergence Analysis of Riemannian Stochastic Approximation Schemes. CoRR Alain Durmus, Pablo Jiménez, Éric Moulines, Salem Said, Hoi-To Wai
2020	J	`jnl`	Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part I: Methodology and Experiments. SIAM J. Imaging Sci. Ana Fernandez Vidal, Valentin De Bortoli, Marcelo Pereyra, Alain Durmus
2020	J	`jnl`	Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach. Part II: Theoretical Analysis. SIAM J. Imaging Sci. Valentin De Bortoli, Alain Durmus, Marcelo Pereyra, Ana Fernandez Vidal
2020	J	`jnl`	MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference. CoRR Achille Thin, Nikita Kotelevskii, Jean-Stanislas Denain, Léo Grinsztajn, Alain Durmus, Maxim Panov, Eric Moulines
2020	A*	`conf`	Quantitative Propagation of Chaos for SGD in Wide Neural Networks. NeurIPS Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli
2020	J	`jnl`	Quantitative Propagation of Chaos for SGD in Wide Neural Networks. CoRR Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli
2020	A*	`conf`	Statistical and Topological Properties of Sliced Probability Divergences. NeurIPS Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Simsekli
2020	J	`jnl`	Statistical and Topological Properties of Sliced Probability Divergences. CoRR Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Simsekli
2019	J	`jnl`	Analysis of Langevin Monte Carlo via Convex Optimization. J. Mach. Learn. Res. Alain Durmus, Szymon Majewski, Blazej Miasojedow
2019	A*	`conf`	Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance. NeurIPS Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau
2019	J	`jnl`	Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance. CoRR Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau
2019	A*	`conf`	Copula-like Variational Inference. NeurIPS Marcel Hirt, Petros Dellaportas, Alain Durmus
2019	J	`jnl`	Copula-like Variational Inference. CoRR Marcel Hirt, Petros Dellaportas, Alain Durmus
2019	—	`conf`	Markov Decision Process for MOOC Users Behavioral Inference. EMOOCs Firas Jarboui, Célya Gruson-Daniel, Alain Durmus, Vincent Rocchisani, Sophie-Helene Goulet Ebongue, Anneliese Depoux, Wilfried Kirschenmann, Vianney Perchet
2019	J	`jnl`	Markov Decision Process for MOOC users behavioral inference. CoRR Firas Jarboui, Célya Gruson-Daniel, Alain Durmus, Vincent Rocchisani, Sophie-Helene Goulet Ebongue, Anneliese Depoux, Wilfried Kirschenmann, Vianney Perchet
2019	J	`jnl`	Maximum entropy methods for texture synthesis: theory and practice. CoRR Valentin De Bortoli, Agnès Desolneux, Alain Durmus, Bruno Galerne, Arthur Leclaire
2019	A*	`conf`	Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions. ICML Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, Fabian-Robert Stöter
2018	J	`jnl`	Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau. SIAM J. Imaging Sci. Alain Durmus, Eric Moulines, Marcelo Pereyra
2018	J	`jnl`	Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions. CoRR Umut Simsekli, Antoine Liutkus, Szymon Majewski, Alain Durmus
2018	A*	`conf`	The promises and pitfalls of Stochastic Gradient Langevin Dynamics. NeurIPS Nicolas Brosse, Alain Durmus, Eric Moulines
2018	J	`jnl`	The promises and pitfalls of Stochastic Gradient Langevin Dynamics. CoRR Nicolas Brosse, Alain Durmus, Eric Moulines
2017	J	`jnl`	Optimal scaling of the random walk Metropolis algorithm under L p mean differentiability. J. Appl. Probab. Alain Durmus, Sylvain Le Corff, Eric Moulines, Gareth O. Roberts
2017	Misc	`conf`	Parallelized Stochastic Gradient Markov Chain Monte Carlo algorithms for non-negative matrix factorization. ICASSP Umut Simsekli, Alain Durmus, Roland Badeau, Gaël Richard, Eric Moulines, A. Taylan Cemgil
2017	A*	`conf`	Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo. COLT Nicolas Brosse, Alain Durmus, Eric Moulines, Marcelo Pereyra
2016	—	`conf`	Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo. NIPS Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël Richard
2015	J	`jnl`	Quantitative bounds of convergence for geometrically ergodic Markov chain in the Wasserstein distance with application to the Metropolis Adjusted Langevin Algorithm. Stat. Comput. Alain Durmus, Eric Moulines
2013	—	`conf`	Lattice Signatures and Bimodal Gaussians. CRYPTO (1) Léo Ducas, Alain Durmus, Tancrède Lepoint, Vadim Lyubashevsky
2013	J	`jnl`	Lattice Signatures and Bimodal Gaussians. IACR Cryptol. ePrint Arch. Léo Ducas, Alain Durmus, Tancrède Lepoint, Vadim Lyubashevsky
2012	—	`conf`	Ring-LWE in Polynomial Rings. Public Key Cryptography Léo Ducas, Alain Durmus
2012	J	`jnl`	Ring-LWE in Polynomial Rings. IACR Cryptol. ePrint Arch. Léo Ducas, Alain Durmus