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)

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)

Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise. - Maxim Kaledin; Eric Moulines; Alexey Naumov; Vladislav Tadic; Hoi-To Wai (COLT, 2020)

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)

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)

Local-Global MCMC kernels: the best of both worlds. - Sergey Samsonov; Evgeny Lagutin; Marylou Gabrié; Alain Durmus; Alexey Naumov; Eric Moulines (NeurIPS, 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 001; Michal Valko; Pierre Ménard (NeurIPS, 2022)

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 (ICML, 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)

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 (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)

Generative Flow Networks as Entropy-Regularized RL. - Daniil Tiapkin; Nikita Morozov; Alexey Naumov; Dmitry P. Vetrov (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)

Demonstration-Regularized RL. - Daniil Tiapkin; Denis Belomestny; Daniele Calandriello; Eric Moulines; Alexey Naumov; Pierre Perrault; Michal Valko; Pierre Ménard (ICLR, 2024)

Group and Shuffle: Efficient Structured Orthogonal Parametrization. - Mikhail Gorbunov; Nikolay Yudin; Vera Soboleva; Aibek Alanov; Alexey Naumov; Maxim Rakhuba (NeurIPS, 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)

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)

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)

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)

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

Sample complexity of Schrödinger potential estimation. - Nikita Puchkin; Iurii Pustovalov; Yuri Sapronov; Denis Suchkov; Alexey Naumov; Denis Belomestny (CoRR, 2025)

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

Tight Bounds for Schrödinger Potential Estimation in Unpaired Image-to-Image Translation Problems. - Nikita Puchkin; Denis Suchkov; Alexey Naumov; Denis Belomestny (CoRR, 2025)