Docking study on mammalian CTR1 copper importer motifs. - Evgeny Zatulovskiy; Sergey Samsonov; Alexei Skvortsov (BMC Syst. Biol., 2007)

Block-based damage assessment of the 2012 Ahar-Varzaghan, Iran, earthquake through SAR remote senisng data. - Sadra Karimzadeh; Sergey Samsonov; Masashi Matsuoka (IGARSS, 2017)

Variance reduction for Markov chains with application to MCMC. - Denis Belomestny; Leonid Iosipoi; Eric Moulines; Alexey Naumov; Sergey Samsonov (Stat. Comput., 2020)

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 (COLT, 2021)

MasTer: A Full Automatic Multi-Satellite InSAR Mass Processing Tool for Rapid Incremental 2D Ground Deformation Time Series. - Nicolas d'Oreye; Dominique Derauw; Sergey Samsonov; Maxime Jaspard; Delphine Smittarello (IGARSS, 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 (NeurIPS, 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)

Ex2MCMC: Sampling through Exploration Exploitation. - Evgeny Lagutin; Daniil Selikhanovych; Achille Thin; Sergey Samsonov; Alexey Naumov; Denis Belomestny; Maxim Panov; Eric Moulines (CoRR, 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)

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 (ICML, 2022)

BR-SNIS: Bias Reduced Self-Normalized Importance Sampling. - Gabriel Cardoso 001; Sergey Samsonov; Achille Thin; Eric Moulines; Jimmy Olsson (NeurIPS, 2022)

Local-Global MCMC kernels: the best of both worlds. - Sergey Samsonov; Evgeny Lagutin; Marylou Gabrié; Alain Durmus; Alexey Naumov; Eric Moulines (NeurIPS, 2022)

Variance reduction for additive functionals of Markov chains via martingale representations. - Denis Belomestny; Eric Moulines; Sergey Samsonov (Stat. Comput., 2022)

Seasonal spatial-temporal variability in radar penetration depth. - Sergey Samsonov (CENTERIS/ProjMAN/HCist, 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 (NeurIPS, 2023)

Finite-Sample Analysis of the Temporal Difference Learning. - Sergey Samsonov; Daniil Tiapkin; Alexey Naumov; Eric Moulines (CoRR, 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, 2023)

Queuing dynamics of asynchronous Federated Learning. - Louis Leconte; Matthieu Jonckheere; Sergey Samsonov; Eric Moulines (AISTATS, 2024)

Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability. - Sergey Samsonov; Daniil Tiapkin; Alexey Naumov; Eric Moulines (COLT, 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 (NeurIPS, 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 (NeurIPS, 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)

Improving GFlowNets with Monte Carlo Tree Search. - Nikita Morozov; Daniil Tiapkin; Sergey Samsonov; Alexey Naumov; Dmitry P. Vetrov (CoRR, 2024)

Refined Analysis of Federated Averaging's Bias and Federated Richardson-Romberg Extrapolation. - Paul Mangold; Alain Durmus; Aymeric Dieuleveut; Sergey Samsonov; Eric Moulines (CoRR, 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)

Theoretical guarantees for neural control variates in MCMC. - Denis Belomestny; Artur Goldman; Alexey Naumov; Sergey Samsonov (Math. Comput. Simul., 2024)

3D Surface Velocity Field Inferred from SAR Interferometry: Cerro Prieto Step-Over, Mexico, Case Study. - Ignacio F. Garcia-Meza; Alejandro González Ortega; Olga Sarychikhina; Eric J. Fielding; Sergey Samsonov (Remote. Sens., 2024)

Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation. - Paul Mangold; Alain Oliviero Durmus; Aymeric Dieuleveut; Sergey Samsonov; Eric Moulines (AISTATS, 2025)

Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization. - Timofei Gritsaev; Nikita Morozov; Sergey Samsonov; Daniil Tiapkin (ICLR, 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 (ICLR, 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)

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, 2025)

Revisiting Non-Acyclic GFlowNets in Discrete Environments. - Nikita Morozov; Ian Maksimov; Daniil Tiapkin; Sergey Samsonov (CoRR, 2025)

Statistical inference for Linear Stochastic Approximation with Markovian Noise. - Sergey Samsonov; Marina Sheshukova; Eric Moulines; Alexey Naumov (CoRR, 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, 2025)

On the Upper Bounds for the Matrix Spectral Norm. - Alexey Naumov; Maxim Rakhuba; Denis Ryapolov; Sergey Samsonov (CoRR, 2025)

Matrix-Free Two-to-Infinity and One-to-Two Norms Estimation. - Askar Tsyganov; Evgeny Frolov; Sergey Samsonov; Maxim Rakhuba (CoRR, 2025)