Sergey Samsonov

Publication Activity (10 Years)

Years Active: 2007-2024
Publications (10 Years): 31

Top Topics

Variance Reduction

Temporal Difference

Metropolis Hastings

Stochastic Approximation

Top Venues

Publications

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 (2024)
Louis Leconte, Matthieu Jonckheere, Sergey Samsonov, Eric Moulines
Queuing dynamics of asynchronous Federated Learning. AISTATS (2024)
Louis Leconte, Matthieu Jonckheere, Sergey Samsonov, Eric Moulines
Queuing dynamics of asynchronous Federated Learning. CoRR (2024)
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 (2024)
Nikita Morozov, Daniil Tiapkin, Sergey Samsonov, Alexey Naumov, Dmitry Vetrov
Improving GFlowNets with Monte Carlo Tree Search. CoRR (2024)
Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines
Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability. COLT (2024)
Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov
Theoretical guarantees for neural control variates in MCMC. Math. Comput. Simul. 220 (2024)
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)
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 (2023)
Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines
Finite-Sample Analysis of the Temporal Difference Learning. CoRR (2023)
Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov
Theoretical guarantees for neural control variates in MCMC. CoRR (2023)
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 (2023)
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 (2022)
Denis Belomestny, Eric Moulines, Sergey Samsonov
Variance reduction for additive functionals of Markov chains via martingale representations. Stat. Comput. 32 (1) (2022)
Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov
Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation. CoRR (2022)
Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling. CoRR (2022)
Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines
Local-Global MCMC kernels: the best of both worlds. NeurIPS (2022)
Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling. NeurIPS (2022)
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 (2022)
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)
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)
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) (2021)
Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines
MCMC: Sampling through Exploration Exploitation. CoRR (2021)
Denis Belomestny, Ilya Levin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Veronika Zorina
Model-free policy evaluation in Reinforcement Learning via upper solutions. CoRR (2021)
Nicolas d'Oreye, Dominique Derauw, Sergey Samsonov, Maxime Jaspard, Delphine Smittarello
MasTer: A Full Automatic Multi-Satellite InSAR Mass Processing Tool for Rapid Incremental 2D Ground Deformation Time Series. IGARSS (2021)
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 (2021)
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)
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 (2021)
Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov
Variance reduction for Markov chains with application to MCMC. Stat. Comput. 30 (4) (2020)
Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov
Variance reduction for Markov chains with application to MCMC. CoRR (2019)
Sadra Karimzadeh, Sergey Samsonov, Masashi Matsuoka
Block-based damage assessment of the 2012 Ahar-Varzaghan, Iran, earthquake through SAR remote senisng data. IGARSS (2017)
Evgeny Zatulovskiy, Sergey Samsonov, Alexei Skvortsov
Docking study on mammalian CTR1 copper importer motifs. BMC Syst. Biol. 1 (S-1) (2007)