| Variance reduction for Markov chains with application to MCMC D Belomestny, L Iosipoi, E Moulines, A Naumov, S Samsonov Statistics and Computing 30, 973-997, 2020 | 24 | 2020 |
| On the stability of random matrix product with markovian noise: Application to linear stochastic approximation and td learning A Durmus, E Moulines, A Naumov, S Samsonov, HT Wai Conference on Learning Theory, 1711-1752, 2021 | 18 | 2021 |
| Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize A Durmus, E Moulines, A Naumov, S Samsonov, K Scaman, HT Wai Advances in Neural Information Processing Systems 34, 30063-30074, 2021 | 17 | 2021 |
| Rates of convergence for density estimation with generative adversarial networks N Puchkin, S Samsonov, D Belomestny, E Moulines, A Naumov Journal of Machine Learning Research 25 (29), 1-47, 2024 | 13* | 2024 |
| Local-Global MCMC kernels: the best of both worlds S Samsonov, E Lagutin, M Gabrié, A Durmus, A Naumov, E Moulines Advances in Neural Information Processing Systems 35, 5178-5193, 2022 | 13* | 2022 |
| From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses D Tiapkin, D Belomestny, É Moulines, A Naumov, S Samsonov, Y Tang, ... International Conference on Machine Learning, 21380-21431, 2022 | 13 | 2022 |
| Finite-Time High-Probability Bounds for Polyak–Ruppert Averaged Iterates of Linear Stochastic Approximation A Durmus, E Moulines, A Naumov, S Samsonov Mathematics of Operations Research, 2024 | 10 | 2024 |
| Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations D Belomestny, A Naumov, N Puchkin, S Samsonov Neural Networks 161, 242-253, 2023 | 10 | 2023 |
| Variance reduction for dependent sequences with applications to stochastic gradient MCMC D Belomestny, L Iosipoi, E Moulines, A Naumov, S Samsonov SIAM/ASA Journal on Uncertainty Quantification 9 (2), 507-535, 2021 | 10 | 2021 |
| Variance reduction for additive functionals of Markov chains via martingale representations D Belomestny, E Moulines, S Samsonov Statistics and Computing 32 (1), 1-22, 2022 | 7 | 2022 |
| First order methods with markovian noise: from acceleration to variational inequalities A Beznosikov, S Samsonov, M Sheshukova, A Gasnikov, A Naumov, ... Advances in Neural Information Processing Systems 36, 2024 | 6 | 2024 |
| Theoretical guarantees for neural control variates in MCMC D Belomestny, A Goldman, A Naumov, S Samsonov Mathematics and Computers in Simulation, 2024 | 3 | 2024 |
| Rosenthal-type inequalities for linear statistics of Markov chains A Durmus, E Moulines, A Naumov, S Samsonov, M Sheshukova arXiv preprint arXiv:2303.05838, 2023 | 3 | 2023 |
| Br-snis: bias reduced self-normalized importance sampling G Cardoso, S Samsonov, A Thin, E Moulines, J Olsson Advances in Neural Information Processing Systems 35, 716-729, 2022 | 3 | 2022 |
| Probability and moment inequalities for additive functionals of geometrically ergodic Markov chains A Durmus, E Moulines, A Naumov, S Samsonov Journal of Theoretical Probability, 1-50, 2024 | 2 | 2024 |
| Estimation of the second moment based on rounded data SV Samsonov, NG Ushakov, VG Ushakov Journal of Mathematical Sciences 237, 819-825, 2019 | 2 | 2019 |
| Finite-Sample Analysis of the Temporal Difference Learning S Samsonov, D Tiapkin, A Naumov, E Moulines arXiv preprint arXiv:2310.14286, 2023 | 1 | 2023 |
| Queuing dynamics of asynchronous Federated Learning L Leconte, M Jonckheere, S Samsonov, E Moulines International Conference on Artificial Intelligence and Statistics, 1711-1719, 2024 | | 2024 |
| SCAFFLSA: Quantifying and Eliminating Heterogeneity Bias in Federated Linear Stochastic Approximation and Temporal Difference Learning P Mangold, S Samsonov, S Labbi, I Levin, R Alami, A Naumov, ... arXiv preprint arXiv:2402.04114, 2024 | | 2024 |
Eric MoulinesProfesseur, Ecole Polytechnique, Membre de l'Académie des SciencesVerified email at polytechnique.edu
