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2020 – today
- 2025
[c16]Marina Sheshukova, Denis Belomestny, Alain Oliviero Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov:
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation. ICLR 2025- [i24]Marina Sheshukova, Sergey Samsonov, Denis Belomestny, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov:
Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent. CoRR abs/2502.06719 (2025) - 2024
- [j8]Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov:
Rates of convergence for density estimation with generative adversarial networks. J. Mach. Learn. Res. 25: 29:1-29:47 (2024) - [j7]Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov
:
Theoretical guarantees for neural control variates in MCMC. Math. Comput. Simul. 220: 382-405 (2024) - [c15]Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov:
Generative Flow Networks as Entropy-Regularized RL. AISTATS 2024: 4213-4221 - [c14]Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines:
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability. COLT 2024: 4511-4547 - [c13]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Demonstration-Regularized RL. ICLR 2024 - [c12]Mikhail Gorbunov, Nikolay Yudin, Vera Soboleva, Aibek Alanov, Alexey Naumov, Maxim Rakhuba:
Group and Shuffle: Efficient Structured Orthogonal Parametrization. NeurIPS 2024 - [c11]Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Réda Alami, Alexey Naumov, Eric Moulines:
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning. NeurIPS 2024 - [c10]Sergey Samsonov, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov:
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning. NeurIPS 2024 - [i23]Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Réda Alami, Alexey Naumov, Eric Moulines:
SCAFFLSA: Quantifying and Eliminating Heterogeneity Bias in Federated Linear Stochastic Approximation and Temporal Difference Learning. CoRR abs/2402.04114 (2024) - [i22]Sergey Samsonov, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov:
Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning. CoRR abs/2405.16644 (2024) - [i21]Mikhail Gorbunov, Nikolay Yudin, Vera Soboleva, Aibek Alanov, Alexey Naumov, Maxim Rakhuba:
Group and Shuffle: Efficient Structured Orthogonal Parametrization. CoRR abs/2406.10019 (2024) - [i20]Nikita Morozov, Daniil Tiapkin, Sergey Samsonov, Alexey Naumov, Dmitry Vetrov:
Improving GFlowNets with Monte Carlo Tree Search. CoRR abs/2406.13655 (2024) - [i19]Pierre Perrault, Denis Belomestny, Pierre Ménard, Éric Moulines, Alexey Naumov, Daniil Tiapkin, Michal Valko:
A New Bound on the Cumulant Generating Function of Dirichlet Processes. CoRR abs/2409.18621 (2024) - [i18]Marina Sheshukova, Denis Belomestny, Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov:
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation. CoRR abs/2410.05106 (2024) - 2023
- [j6]Denis Belomestny, Alexey Naumov, Nikita Puchkin, Sergey Samsonov:
Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations. Neural Networks 161: 242-253 (2023) - [c9]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard:
Fast Rates for Maximum Entropy Exploration. ICML 2023: 34161-34221 - [c8]Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander V. Gasnikov, Alexey Naumov, Eric Moulines:
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities. NeurIPS 2023 - [c7]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Model-free Posterior Sampling via Learning Rate Randomization. NeurIPS 2023 - [i17]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard:
Fast Rates for Maximum Entropy Exploration. CoRR abs/2303.08059 (2023) - [i16]Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov:
Theoretical guarantees for neural control variates in MCMC. CoRR abs/2304.01111 (2023) - [i15]Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander V. Gasnikov, Alexey Naumov, Eric Moulines:
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities. CoRR abs/2305.15938 (2023) - [i14]Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov:
Generative Flow Networks as Entropy-Regularized RL. CoRR abs/2310.12934 (2023) - [i13]Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines:
Finite-Sample Analysis of the Temporal Difference Learning. CoRR abs/2310.14286 (2023) - [i12]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Demonstration-Regularized RL. CoRR abs/2310.17303 (2023) - [i11]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard:
Model-free Posterior Sampling via Learning Rate Randomization. CoRR abs/2310.18186 (2023) - 2022
- [c6]Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard:
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. ICML 2022: 21380-21431 - [c5]Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines:
Local-Global MCMC kernels: the best of both worlds. NeurIPS 2022 - [c4]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard:
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS 2022 - [i10]Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard:
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. CoRR abs/2205.07704 (2022) - [i9]Denis Belomestny, Alexey Naumov, Nikita Puchkin, Sergey Samsonov:
Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations. CoRR abs/2206.09527 (2022) - [i8]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) - [i7]Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard:
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR abs/2209.14414 (2022) - 2021
- [j5]Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov:
Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC. SIAM/ASA J. Uncertain. Quantification 9(2): 507-535 (2021) - [c3]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 - [c2]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 - [i6]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) - [i5]Denis Belomestny, Ilya Levin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Veronika Zorina:
Model-free policy evaluation in Reinforcement Learning via upper solutions. CoRR abs/2105.02135 (2021) - [i4]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) - [i3]Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines:
Ex2MCMC: Sampling through Exploration Exploitation. CoRR abs/2111.02702 (2021) - 2020
- [j4]Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov:
Variance reduction for Markov chains with application to MCMC. Stat. Comput. 30(4): 973-997 (2020) - [c1]Maxim Kaledin, Eric Moulines, Alexey Naumov, Vladislav Tadic, Hoi-To Wai:
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise. COLT 2020: 2144-2203 - [i2]Maxim Kaledin, Eric Moulines, Alexey Naumov, Vladislav Tadic, Hoi-To Wai:
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise. CoRR abs/2002.01268 (2020)
2010 – 2019
- 2019
- [i1]Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov:
Variance reduction for Markov chains with application to MCMC. CoRR abs/1910.03643 (2019) - 2017
- [j3]Scott A. Ochsner, Yolanda F. Darlington, Apollo McOwiti, Wasula H. Kankanamge, Alexey Naumov, Lauren B. Becnel, Neil J. McKenna:
A FAIR-Based Approach to Enhancing the Discovery and Re-Use of Transcriptomic Data Assets for Nuclear Receptor Signaling Pathways. Data Sci. J. 16: 7 (2017) - [j2]Scott A. Ochsner, Yolanda F. Darlington, Apollo McOwiti, Wasula H. Kankanamge, Alexey Naumov, Lauren B. Becnel, Neil J. McKenna:
A FAIR-Based Approach to Enhancing the Discovery and Re-Use of Transcriptomic Data Assets for Nuclear Receptor Signaling Pathways. Data Sci. J. 16: 11 (2017) - [j1]Yolanda F. Darlington, Alexey Naumov, Apollo McOwiti, Wasula H. Kankanamge, Lauren B. Becnel, Neil J. McKenna:
Improving the discoverability, accessibility, and citability of omics datasets: a case report. J. Am. Medical Informatics Assoc. 24(2): 388-393 (2017)
Coauthor Index
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