Sergey Samsonov

64 papers A* 16A 2C 2Journal 43Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	Gaussian Approximation for Two-Timescale Linear Stochastic Approximation. AAAI Bogdan Butyrin, Artemy Rubtsov, Alexey Naumov, Vladimir V. Ulyanov, Sergey Samsonov
2026	A*	`conf`	High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation. AAAI Ilya Levin, Alexey Naumov, Sergey Samsonov
2026	J	`jnl`	Learning Shortest Paths with Generative Flow Networks. CoRR Nikita Morozov, Ian Maksimov, Daniil Tiapkin, Sergey Samsonov
2026	A*	`conf`	Matrix-Free Two-to-Infinity and One-to-Two Norms Estimation. AAAI Askar Tsyganov, Evgeny Frolov, Sergey Samsonov, Maxim Rakhuba
2026	A*	`conf`	Revisiting IPS-based Algorithms for Off-Policy Evaluation of Contextual Bandits. WWW Daria Korovaitceva, Marina Sheshukova, Evgeny Frolov, Sergey Samsonov
2026	J	`jnl`	UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms. J. Optim. Theory Appl. Denis Belomestny, Ilya Levin, Alexey Naumov, Sergey Samsonov
2025	J	`jnl`	Adaptive Destruction Processes for Diffusion Samplers. CoRR Timofei Gritsaev, Nikita Morozov, Kirill Tamogashev, Daniil Tiapkin, Sergey Samsonov, Alexey Naumov, Dmitry P. Vetrov, Nikolay Malkin
2025	J	`jnl`	Finite-Time High-Probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation. Math. Oper. Res. Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov
2025	J	`jnl`	Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent. CoRR Marina Sheshukova, Sergey Samsonov, Denis Belomestny, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov
2025	J	`jnl`	Gaussian Approximation for Two-Timescale Linear Stochastic Approximation. CoRR Bogdan Butyrin, Artemy Rubtsov, Alexey Naumov, Vladimir V. Ulyanov, Sergey Samsonov
2025	J	`jnl`	High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation. CoRR Ilya Levin, Alexey Naumov, Sergey Samsonov
2025	J	`jnl`	Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation. CoRR Bogdan Butyrin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Qi-Man Shao, Zhuo-Song Zhang
2025	J	`jnl`	Matrix-Free Two-to-Infinity and One-to-Two Norms Estimation. CoRR Askar Tsyganov, Evgeny Frolov, Sergey Samsonov, Maxim V. Rakhuba
2025	A*	`conf`	Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation. ICLR Marina Sheshukova, Denis Belomestny, Alain Oliviero Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov
2025	J	`jnl`	On the Rate of Gaussian Approximation for Linear Regression Problems. CoRR Marat Khusainov, Marina Sheshukova, Alain Durmus, Sergey Samsonov
2025	J	`jnl`	On the Upper Bounds for the Matrix Spectral Norm. CoRR Alexey Naumov, Maxim V. Rakhuba, Denis Ryapolov, Sergey Samsonov
2025	A*	`conf`	Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization. ICLR Timofei Gritsaev, Nikita Morozov, Sergey Samsonov, Daniil Tiapkin
2025	A	`conf`	Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation. AISTATS Paul Mangold, Alain Oliviero Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines
2025	A*	`conf`	Revisiting Non-Acyclic GFlowNets in Discrete Environments. ICML Nikita Morozov, Ian Maksimov, Daniil Tiapkin, Sergey Samsonov
2025	J	`jnl`	Revisiting Non-Acyclic GFlowNets in Discrete Environments. CoRR Nikita Morozov, Ian Maksimov, Daniil Tiapkin, Sergey Samsonov
2025	J	`jnl`	Scalable LinUCB: Low-Rank Design Matrix Updates for Recommenders with Large Action Spaces. CoRR Evgenia Shustova, Marina Sheshukova, Sergey Samsonov, Evgeny Frolov
2025	J	`jnl`	Statistical analysis of Inverse Entropy-regularized Reinforcement Learning. CoRR Denis Belomestny, Alexey Naumov, Sergey Samsonov
2025	J	`jnl`	Statistical inference for Linear Stochastic Approximation with Markovian Noise. CoRR Sergey Samsonov, Marina Sheshukova, Eric Moulines, Alexey Naumov
2025	J	`jnl`	gfnx: Fast and Scalable Library for Generative Flow Networks in JAX. CoRR Daniil Tiapkin, Artem Agarkov, Nikita Morozov, Ian Maksimov, Askar Tsyganov, Timofei Gritsaev, Sergey Samsonov
2024	J	`jnl`	3D Surface Velocity Field Inferred from SAR Interferometry: Cerro Prieto Step-Over, Mexico, Case Study. Remote. Sens. Ignacio F. Garcia-Meza, Alejandro González Ortega, Olga Sarychikhina, Eric J. Fielding, Sergey Samsonov
2024	A*	`conf`	Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning. NeurIPS Sergey Samsonov, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov
2024	J	`jnl`	Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning. CoRR Sergey Samsonov, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov
2024	A*	`conf`	Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability. COLT Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines
2024	J	`jnl`	Improving GFlowNets with Monte Carlo Tree Search. CoRR Nikita Morozov, Daniil Tiapkin, Sergey Samsonov, Alexey Naumov, Dmitry P. Vetrov
2024	J	`jnl`	Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation. CoRR Marina Sheshukova, Denis Belomestny, Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov
2024	J	`jnl`	Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization. CoRR Timofei Gritsaev, Nikita Morozov, Sergey Samsonov, Daniil Tiapkin
2024	A	`conf`	Queuing dynamics of asynchronous Federated Learning. AISTATS Louis Leconte, Matthieu Jonckheere, Sergey Samsonov, Eric Moulines
2024	J	`jnl`	Queuing dynamics of asynchronous Federated Learning. CoRR Louis Leconte, Matthieu Jonckheere, Sergey Samsonov, Eric Moulines
2024	J	`jnl`	Rates of convergence for density estimation with generative adversarial networks. J. Mach. Learn. Res. Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov
2024	J	`jnl`	Refined Analysis of Federated Averaging's Bias and Federated Richardson-Romberg Extrapolation. CoRR Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines
2024	J	`jnl`	SCAFFLSA: Quantifying and Eliminating Heterogeneity Bias in Federated Linear Stochastic Approximation and Temporal Difference Learning. CoRR Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Réda Alami, Alexey Naumov, Eric Moulines
2024	A*	`conf`	SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning. NeurIPS Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Réda Alami, Alexey Naumov, Eric Moulines
2024	J	`jnl`	Theoretical guarantees for neural control variates in MCMC. Math. Comput. Simul. Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov
2023	J	`jnl`	Finite-Sample Analysis of the Temporal Difference Learning. CoRR Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines
2023	A*	`conf`	First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities. NeurIPS Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander V. Gasnikov, Alexey Naumov, Eric Moulines
2023	J	`jnl`	First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities. CoRR Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander V. Gasnikov, Alexey Naumov, Eric Moulines
2023	—	`conf`	Seasonal spatial-temporal variability in radar penetration depth. CENTERIS/ProjMAN/HCist Sergey Samsonov
2023	J	`jnl`	Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations. Neural Networks Denis Belomestny, Alexey Naumov, Nikita Puchkin, Sergey Samsonov
2023	J	`jnl`	Theoretical guarantees for neural control variates in MCMC. CoRR Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov
2022	A*	`conf`	BR-SNIS: Bias Reduced Self-Normalized Importance Sampling. NeurIPS Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson
2022	J	`jnl`	BR-SNIS: Bias Reduced Self-Normalized Importance Sampling. CoRR Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson
2022	J	`jnl`	Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation. CoRR Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov
2022	A*	`conf`	From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. ICML Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
2022	J	`jnl`	From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses. CoRR Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard
2022	A*	`conf`	Local-Global MCMC kernels: the best of both worlds. NeurIPS Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines
2022	J	`jnl`	Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations. CoRR Denis Belomestny, Alexey Naumov, Nikita Puchkin, Sergey Samsonov
2022	J	`jnl`	Variance reduction for additive functionals of Markov chains via martingale representations. Stat. Comput. Denis Belomestny, Eric Moulines, Sergey Samsonov
2021	J	`jnl`	Ex CoRR Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines
2021	C	`conf`	MasTer: A Full Automatic Multi-Satellite InSAR Mass Processing Tool for Rapid Incremental 2D Ground Deformation Time Series. IGARSS Nicolas d'Oreye, Dominique Derauw, Sergey Samsonov, Maxime Jaspard, Delphine Smittarello
2021	J	`jnl`	Model-free policy evaluation in Reinforcement Learning via upper solutions. CoRR Denis Belomestny, Ilya Levin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Veronika Zorina
2021	A*	`conf`	On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning. COLT Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Hoi-To Wai
2021	J	`jnl`	On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning. CoRR Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Hoi-To Wai
2021	A*	`conf`	Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize. NeurIPS Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai
2021	J	`jnl`	Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize. CoRR Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai
2021	J	`jnl`	Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC. SIAM/ASA J. Uncertain. Quantification Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov
2020	J	`jnl`	Variance reduction for Markov chains with application to MCMC. Stat. Comput. Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov
2019	J	`jnl`	Variance reduction for Markov chains with application to MCMC. CoRR Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov
2017	C	`conf`	Block-based damage assessment of the 2012 Ahar-Varzaghan, Iran, earthquake through SAR remote senisng data. IGARSS Sadra Karimzadeh, Sergey Samsonov, Masashi Matsuoka
2007	J	`jnl`	Docking study on mammalian CTR1 copper importer motifs. BMC Syst. Biol. Evgeny Zatulovskiy, Sergey Samsonov, Alexei Skvortsov