Alain Durmus
According to our database1,
Alain Durmus
authored at least 64 papers
between 2012 and 2024.
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Bibliography
2024
CoRR, 2024
2023
On Maximum a Posteriori Estimation with Plug & Play Priors and Stochastic Gradient Descent.
J. Math. Imaging Vis., January, 2023
CoRR, 2023
CoRR, 2023
Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Proceedings of the International Conference on Machine Learning, 2023
Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023
2022
A Proximal Markov Chain Monte Carlo Method for Bayesian Inference in Imaging Inverse Problems: When Langevin Meets Moreau.
SIAM Rev., 2022
SIAM J. Imaging Sci., 2022
Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation.
CoRR, 2022
Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study.
CoRR, 2022
Boost your favorite Markov Chain Monte Carlo sampler using Kac's theorem: the Kick-Kac teleportation algorithm.
CoRR, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022
2021
SIAM J. Math. Data Sci., 2021
Stat. Comput., 2021
CoRR, 2021
Uniform minorization condition and convergence bounds for discretizations of kinetic Langevin dynamics.
CoRR, 2021
Proceedings of the IEEE Statistical Signal Processing Workshop, 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the 38th International Conference on Machine Learning, 2021
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs.
Proceedings of the 38th International Conference on Machine Learning, 2021
Convergence rates and approximation results for SGD and its continuous-time counterpart.
Proceedings of the Conference on Learning Theory, 2021
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning.
Proceedings of the Conference on Learning Theory, 2021
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021
2020
Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach Part I: Methodology and Experiments.
SIAM J. Imaging Sci., 2020
Maximum Likelihood Estimation of Regularization Parameters in High-Dimensional Inverse Problems: An Empirical Bayesian Approach. Part II: Theoretical Analysis.
SIAM J. Imaging Sci., 2020
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference.
CoRR, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020
2019
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions.
Proceedings of the 36th International Conference on Machine Learning, 2019
Proceedings of the Digital Education: At the MOOC Crossroads Where the Interests of Academia and Business Converge, 2019
2018
Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau.
SIAM J. Imaging Sci., 2018
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions.
CoRR, 2018
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018
2017
Optimal scaling of the random walk Metropolis algorithm under L p mean differentiability.
J. Appl. Probab., 2017
Parallelized Stochastic Gradient Markov Chain Monte Carlo algorithms for non-negative matrix factorization.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo.
Proceedings of the 30th Conference on Learning Theory, 2017
2016
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016
2015
Quantitative bounds of convergence for geometrically ergodic Markov chain in the Wasserstein distance with application to the Metropolis Adjusted Langevin Algorithm.
Stat. Comput., 2015
2013
2012