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ORCIDAlain Durmus
Alain Oliviero Durmus
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2020 – today
- 2024
[c39]Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob R. Gardner:
Stochastic Approximation with Biased MCMC for Expectation Maximization. AISTATS 2024: 2332-2340- [c38]Tom Sander, Maxime Sylvestre, Alain Durmus:
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training. AISTATS 2024: 3295-3303 - [c37]Pierre Clavier, Tom Huix, Alain Oliviero Durmus:
VITS : Variational Inference Thompson Sampling for contextual bandits. ICML 2024 - [c36]Louis Grenioux, Maxence Noble, Marylou Gabrié, Alain Oliviero Durmus:
Stochastic Localization via Iterative Posterior Sampling. ICML 2024 - [c35]Tom Huix, Anna Korba, Alain Oliviero Durmus, Eric Moulines:
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians. ICML 2024 - [c34]Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Oliviero Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
Differentially Private Representation Learning via Image Captioning. ICML 2024 - [c33]Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Oliviero Durmus:
Incentivized Learning in Principal-Agent Bandit Games. ICML 2024 - [i48]Tom Sander, Maxime Sylvestre, Alain Durmus:
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training. CoRR abs/2402.08344 (2024) - [i47]Louis Grenioux, Maxence Noble, Marylou Gabrié, Alain Oliviero Durmus:
Stochastic Localization via Iterative Posterior Sampling. CoRR abs/2402.10758 (2024) - [i46]Tom Sander, Pierre Fernandez, Alain Durmus, Matthijs Douze, Teddy Furon:
Watermarking Makes Language Models Radioactive. CoRR abs/2402.14904 (2024) - [i45]Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Jacob R. Gardner:
Stochastic Approximation with Biased MCMC for Expectation Maximization. CoRR abs/2402.17870 (2024) - [i44]Tom Sander, Yaodong Yu, Maziar Sanjabi, Alain Durmus, Yi Ma, Kamalika Chaudhuri, Chuan Guo:
Differentially Private Representation Learning via Image Captioning. CoRR abs/2403.02506 (2024) - [i43]Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, El Mahdi El Mhamdi, Eric Moulines, Michael I. Jordan, Alain Durmus:
Incentivized Learning in Principal-Agent Bandit Games. CoRR abs/2403.03811 (2024) - [i42]Yazid Janati El Idrissi, Alain Durmus, Eric Moulines, Jimmy Olsson:
Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors. CoRR abs/2403.11407 (2024) - [i41]Tom Huix, Anna Korba, Alain Durmus, Eric Moulines:
Theoretical Guarantees for Variational Inference with Fixed-Variance Mixture of Gaussians. CoRR abs/2406.04012 (2024) - [i40]Antoine Scheid, Aymeric Capitaine, Etienne Boursier, Eric Moulines, Michael I. Jordan, Alain Durmus:
Mitigating Externalities while Learning: an Online Version of the Coase Theorem. CoRR abs/2406.19824 (2024) - [i39]Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus:
Unravelling in Collaborative Learning. CoRR abs/2407.14332 (2024) - [i38]Dario Shariatian, Umut Simsekli, Alain Durmus:
Denoising Lévy Probabilistic Models. CoRR abs/2407.18609 (2024) - [i37]Andrea Bertazzi, Alain Oliviero Durmus, Dario Shariatian, Umut Simsekli, Eric Moulines:
Piecewise deterministic generative models. CoRR abs/2407.19448 (2024) - [i36]Marta Gentiloni Silveri, Giovanni Conforti, Alain Durmus:
Theoretical guarantees in KL for Diffusion Flow Matching. CoRR abs/2409.08311 (2024) - 2023
- [j11]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): 140-163 (2023) - [c32]Vincent Plassier, Eric Moulines, Alain Durmus:
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. AISTATS 2023: 5299-5356 - [c31]Tom Huix, Matthew Zhang, Alain Durmus:
Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo. AISTATS 2023: 8749-8770 - [c30]Giacomo Greco, Maxence Noble, Giovanni Conforti, Alain Durmus:
Non-asymptotic convergence bounds for Sinkhorn iterates and their gradients: a coupling approach. COLT 2023: 716-746 - [c29]Louis Grenioux, Alain Oliviero Durmus, Eric Moulines, Marylou Gabrié:
On Sampling with Approximate Transport Maps. ICML 2023: 11698-11733 - [c28]Kruno Lehman, Alain Durmus, Umut Simsekli:
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent. NeurIPS 2023 - [c27]Maxence Noble, Valentin De Bortoli, Alain Durmus:
Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo. NeurIPS 2023 - [c26]Maxence Noble, Valentin De Bortoli, Arnaud Doucet, Alain Durmus:
Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters. NeurIPS 2023 - [i35]Louis Grenioux, Alain Durmus, Éric Moulines, Marylou Gabrié:
On Sampling with Approximate Transport Maps. CoRR abs/2302.04763 (2023) - [i34]Maxence Noble, Valentin De Bortoli, Arnaud Doucet, Alain Durmus:
Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters. CoRR abs/2305.16557 (2023) - [i33]Evan Camrud, Alain Oliviero Durmus, Pierre Monmarché, Gabriel Stoltz:
Second order quantitative bounds for unadjusted generalized Hamiltonian Monte Carlo. CoRR abs/2306.09513 (2023) - [i32]Pierre Clavier, Tom Huix, Alain Durmus:
VITS : Variational Inference Thomson Sampling for contextual bandits. CoRR abs/2307.10167 (2023) - [i31]Krunoslav Lehman Pavasovic, Alain Durmus, Umut Simsekli:
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent. CoRR abs/2310.18455 (2023) - 2022
- [j10]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): 701-737 (2022) - [j9]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): 991-1028 (2022) - [c25]Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines:
QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. AISTATS 2022: 6459-6500 - [c24]Nikita Kotelevskii, Maxime Vono, Alain Durmus, Eric Moulines:
FedPop: A Bayesian Approach for Personalised Federated Learning. NeurIPS 2022 - [c23]Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines:
Local-Global MCMC kernels: the best of both worlds. NeurIPS 2022 - [i30]Alain Durmus, Éric Moulines:
On the geometric convergence for MALA under verifiable conditions. CoRR abs/2201.01951 (2022) - [i29]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 abs/2201.05002 (2022) - [i28]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 abs/2201.06133 (2022) - [i27]Nikita Kotelevskii, Maxime Vono, Eric Moulines, Alain Durmus:
FedPop: A Bayesian Approach for Personalised Federated Learning. CoRR abs/2206.03611 (2022) - [i26]Tom Huix, Szymon Majewski, Alain Durmus, Eric Moulines, Anna Korba:
Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study. CoRR abs/2207.03859 (2022) - [i25]Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov:
Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation. CoRR abs/2207.04475 (2022) - [i24]Maxence Noble, Valentin De Bortoli, Alain Durmus:
Barrier Hamiltonian Monte Carlo. CoRR abs/2210.11925 (2022) - [i23]Vincent Plassier, Alain Durmus, Eric Moulines:
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. CoRR abs/2211.00100 (2022) - 2021
- [j8]Valentin De Bortoli, Alain Durmus, Marcelo Pereyra, Ana Fernandez Vidal:
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Stat. Comput. 31(3): 29 (2021) - [j7]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): 52-82 (2021) - [c22]Alain Durmus, Pablo Jiménez, Eric Moulines, Salem Said:
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size. AISTATS 2021: 1018-1026 - [c21]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: 1711-1752 - [c20]Xavier Fontaine, Valentin De Bortoli, Alain Durmus:
Convergence rates and approximation results for SGD and its continuous-time counterpart. COLT 2021: 1965-2058 - [c19]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: 8577-8587 - [c18]Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov:
Monte Carlo Variational Auto-Encoders. ICML 2021: 10247-10257 - [c17]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: 12411-12424 - [c16]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: 17060-17071 - [c15]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: 30063-30074 - [c14]Ana Fernandez Vidal, Marcelo Pereyra, Alain Durmus, Jean-François Giovannelli:
Fast Bayesian Model Selection in Imaging Inverse Problems Using Residuals. SSP 2021: 91-95 - [i22]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 abs/2102.00185 (2021) - [i21]Alain Durmus, Pablo Jiménez, Éric Moulines, Salem Said:
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size. CoRR abs/2102.07586 (2021) - [i20]Rémi Laumont, Valentin De Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra:
Bayesian imaging using Plug & Play priors: when Langevin meets Tweedie. CoRR abs/2103.04715 (2021) - [i19]Maxime Vono, Vincent Plassier, Alain Durmus, Aymeric Dieuleveut, Eric Moulines:
QLSD: Quantised Langevin stochastic dynamics for Bayesian federated learning. CoRR abs/2106.00797 (2021) - [i18]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 abs/2106.01257 (2021) - [i17]Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines:
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm. CoRR abs/2106.06300 (2021) - [i16]Kimia Nadjahi, Alain Durmus, Pierre E. Jacob, Roland Badeau, Umut Simsekli:
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections. CoRR abs/2106.15427 (2021) - [i15]Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov:
Monte Carlo Variational Auto-Encoders. CoRR abs/2106.15921 (2021) - [i14]Alain Durmus, Aurélien Enfroy, Éric Moulines, Gabriel Stoltz:
Uniform minorization condition and convergence bounds for discretizations of kinetic Langevin dynamics. CoRR abs/2107.14542 (2021) - [i13]Alain Durmus, Andreas Eberle:
Asymptotic bias of inexact Markov Chain Monte Carlo methods in high dimension. CoRR abs/2108.00682 (2021) - 2020
- [j6]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): 1945-1989 (2020) - [j5]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): 1990-2028 (2020) - [c13]Kimia Nadjahi, Valentin De Bortoli, Alain Durmus, Roland Badeau, Umut Simsekli:
Approximate Bayesian Computation with the Sliced-Wasserstein Distance. ICASSP 2020: 5470-5474 - [c12]Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli:
Quantitative Propagation of Chaos for SGD in Wide Neural Networks. NeurIPS 2020 - [c11]Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Simsekli:
Statistical and Topological Properties of Sliced Probability Divergences. NeurIPS 2020 - [i12]Achille Thin, Nikita Kotelevskii, Jean-Stanislas Denain, Léo 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 abs/2002.12253 (2020) - [i11]Kimia Nadjahi, Alain Durmus, Lénaïc Chizat, Soheil Kolouri, Shahin Shahrampour, Umut Simsekli:
Statistical and Topological Properties of Sliced Probability Divergences. CoRR abs/2003.05783 (2020) - [i10]Alain Durmus, Pablo Jiménez, Éric Moulines, Salem Said, Hoi-To Wai:
Convergence Analysis of Riemannian Stochastic Approximation Schemes. CoRR abs/2005.13284 (2020) - [i9]Valentin De Bortoli, Alain Durmus, Xavier Fontaine, Umut Simsekli:
Quantitative Propagation of Chaos for SGD in Wide Neural Networks. CoRR abs/2007.06352 (2020)
2010 – 2019
- 2019
- [j4]Alain Durmus, Szymon Majewski, Blazej Miasojedow:
Analysis of Langevin Monte Carlo via Convex Optimization. J. Mach. Learn. Res. 20: 73:1-73:46 (2019) - [c10]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: 70-80 - [c9]Antoine Liutkus, Umut Simsekli, Szymon Majewski, Alain Durmus, Fabian-Robert Stöter:
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions. ICML 2019: 4104-4113 - [c8]Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau:
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance. NeurIPS 2019: 250-260 - [c7]Marcel Hirt, Petros Dellaportas, Alain Durmus:
Copula-like Variational Inference. NeurIPS 2019: 2955-2967 - [i8]Marcel Hirt, Petros Dellaportas, Alain Durmus:
Copula-like Variational Inference. CoRR abs/1904.07153 (2019) - [i7]Kimia Nadjahi, Alain Durmus, Umut Simsekli, Roland Badeau:
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance. CoRR abs/1906.04516 (2019) - [i6]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 abs/1907.04723 (2019) - [i5]Valentin De Bortoli, Agnès Desolneux, Alain Durmus, Bruno Galerne, Arthur Leclaire:
Maximum entropy methods for texture synthesis: theory and practice. CoRR abs/1912.01691 (2019) - 2018
- [j3]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): 473-506 (2018) - [c6]Nicolas Brosse, Alain Durmus, Eric Moulines:
The promises and pitfalls of Stochastic Gradient Langevin Dynamics. NeurIPS 2018: 8278-8288 - [i4]Umut Simsekli, Antoine Liutkus, Szymon Majewski, Alain Durmus:
Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions. CoRR abs/1806.08141 (2018) - [i3]Nicolas Brosse, Alain Durmus, Eric Moulines:
The promises and pitfalls of Stochastic Gradient Langevin Dynamics. CoRR abs/1811.10072 (2018) - 2017
- [j2]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): 1233-1260 (2017) - [c5]Nicolas Brosse, Alain Durmus, Eric Moulines, Marcelo Pereyra:
Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo. COLT 2017: 319-342 - [c4]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: 2242-2246 - 2016
- [c3]Alain Durmus, Umut Simsekli, Eric Moulines, Roland Badeau, Gaël Richard:
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo. NIPS 2016: 2047-2055 - 2015
- [j1]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): 5-19 (2015) - 2013
- [c2]Léo Ducas, Alain Durmus, Tancrède Lepoint, Vadim Lyubashevsky:
Lattice Signatures and Bimodal Gaussians. CRYPTO (1) 2013: 40-56 - [i2]Léo Ducas, Alain Durmus, Tancrède Lepoint, Vadim Lyubashevsky:
Lattice Signatures and Bimodal Gaussians. IACR Cryptol. ePrint Arch. 2013: 383 (2013) - 2012
- [c1]Léo Ducas, Alain Durmus:
Ring-LWE in Polynomial Rings. Public Key Cryptography 2012: 34-51 - [i1]Léo Ducas, Alain Durmus:
Ring-LWE in Polynomial Rings. IACR Cryptol. ePrint Arch. 2012: 235 (2012)
Coauthor Index
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