Alexey Naumov

According to our database, Alexey Naumov authored at least 39 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Gaussian Approximation for Two-Timescale Linear Stochastic Approximation.

Bogdan Butyrin, Artemy Rubtsov, Alexey Naumov, Vladimir Ulyanov, Sergey Samsonov

CoRR, August, 2025

Tight Bounds for Schrödinger Potential Estimation in Unpaired Image-to-Image Translation Problems.

Nikita Puchkin, Denis Suchkov, Alexey Naumov, Denis Belomestny

CoRR, August, 2025

High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation.

Ilya Levin, Alexey Naumov, Sergey Samsonov

CoRR, August, 2025

On the Upper Bounds for the Matrix Spectral Norm.

Alexey Naumov, Maxim Rakhuba, Denis Ryapolov, Sergey Samsonov

CoRR, June, 2025

Sample complexity of Schrödinger potential estimation.

Nikita Puchkin, Iurii Pustovalov, Yuri Sapronov, Denis Suchkov, Alexey Naumov, Denis Belomestny

CoRR, June, 2025

Adaptive Destruction Processes for Diffusion Samplers.

Timofei Gritsaev, Nikita Morozov, Kirill Tamogashev, Daniil Tiapkin, Sergey Samsonov, Alexey Naumov, Dmitry P. Vetrov, Nikolay Malkin

CoRR, June, 2025

Accelerating Nash Learning from Human Feedback via Mirror Prox.

Daniil Tiapkin, Daniele Calandriello, Denis Belomestny, Eric Moulines, Alexey Naumov, Kashif Rasul, Michal Valko, Pierre Ménard

CoRR, May, 2025

Statistical inference for Linear Stochastic Approximation with Markovian Noise.

Sergey Samsonov, Marina Sheshukova, Eric Moulines, Alexey Naumov

CoRR, May, 2025

Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent.

Marina Sheshukova, Sergey Samsonov, Denis Belomestny, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov

CoRR, February, 2025

Finite-Time High-Probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation.

Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov

Math. Oper. Res., 2025

Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation.

Marina Sheshukova, Denis Belomestny, Alain Oliviero Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Theoretical guarantees for neural control variates in MCMC.

Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov

Math. Comput. Simul., 2024

Rates of convergence for density estimation with generative adversarial networks.

Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov

J. Mach. Learn. Res., 2024

A New Bound on the Cumulant Generating Function of Dirichlet Processes.

Pierre Perrault, Denis Belomestny, Pierre Ménard, Éric Moulines, Alexey Naumov, Daniil Tiapkin, Michal Valko

CoRR, 2024

Improving GFlowNets with Monte Carlo Tree Search.

Nikita Morozov, Daniil Tiapkin, Sergey Samsonov, Alexey Naumov, Dmitry P. Vetrov

CoRR, 2024

SCAFFLSA: Quantifying and Eliminating Heterogeneity Bias in Federated Linear Stochastic Approximation and Temporal Difference Learning.

Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Réda Alami, Alexey Naumov, Eric Moulines

CoRR, 2024

Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning.

Sergey Samsonov, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning.

Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Réda Alami, Alexey Naumov, Eric Moulines

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Group and Shuffle: Efficient Structured Orthogonal Parametrization.

Mikhail Gorbunov, Nikolay Yudin, Vera Soboleva, Aibek Alanov, Alexey Naumov, Maxim Rakhuba

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Demonstration-Regularized RL.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability.

Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

Generative Flow Networks as Entropy-Regularized RL.

Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations.

Denis Belomestny, Alexey Naumov, Nikita Puchkin, Sergey Samsonov

Neural Networks, April, 2023

Finite-Sample Analysis of the Temporal Difference Learning.

Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines

CoRR, 2023

Model-free Posterior Sampling via Learning Rate Randomization.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities.

Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander V. Gasnikov, Alexey Naumov, Eric Moulines

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fast Rates for Maximum Entropy Exploration.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard

Proceedings of the International Conference on Machine Learning, 2023

2022

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Local-Global MCMC kernels: the best of both worlds.

Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses.

Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard

Proceedings of the International Conference on Machine Learning, 2022

2021

Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC.

Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov

SIAM/ASA J. Uncertain. Quantification, 2021

Ex²MCMC: Sampling through Exploration Exploitation.

Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines

CoRR, 2021

Model-free policy evaluation in Reinforcement Learning via upper solutions.

Denis Belomestny, Ilya Levin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Veronika Zorina

CoRR, 2021

Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize.

Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning.

Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Hoi-To Wai

Proceedings of the Conference on Learning Theory, 2021

2020

Variance reduction for Markov chains with application to MCMC.

Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov

Stat. Comput., 2020

Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise.

Maxim Kaledin, Eric Moulines, Alexey Naumov, Vladislav Tadic, Hoi-To Wai

Proceedings of the Conference on Learning Theory, 2020

2017

Improving the discoverability, accessibility, and citability of omics datasets: a case report.

Yolanda F. Darlington, Alexey Naumov, Apollo McOwiti, Wasula H. Kankanamge, Lauren B. Becnel, Neil J. McKenna

J. Am. Medical Informatics Assoc., 2017

A FAIR-Based Approach to Enhancing the Discovery and Re-Use of Transcriptomic Data Assets for Nuclear Receptor Signaling Pathways.

Scott A. Ochsner, Yolanda F. Darlington, Apollo McOwiti, Wasula H. Kankanamge, Alexey Naumov, Lauren B. Becnel, Neil J. McKenna

Data Sci. J., 2017