Alexey Naumov - RankMe

63 papers A* 17A 1Journal 45

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`	Schrödinger bridge problem via empirical risk minimization. CoRR Denis Belomestny, Alexey Naumov, Nikita Puchkin, Denis Suchkov
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`	Accelerating Nash Learning from Human Feedback via Mirror Prox. CoRR Daniil Tiapkin, Daniele Calandriello, Denis Belomestny, Eric Moulines, Alexey Naumov, Kashif Rasul, Michal Valko, Pierre Ménard
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	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 Upper Bounds for the Matrix Spectral Norm. CoRR Alexey Naumov, Maxim V. Rakhuba, Denis Ryapolov, Sergey Samsonov
2025	J	`jnl`	Sample complexity of Schrödinger potential estimation. CoRR Nikita Puchkin, Iurii Pustovalov, Yuri Sapronov, Denis Suchkov, Alexey Naumov, Denis Belomestny
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`	Tight Bounds for Schrödinger Potential Estimation in Unpaired Image-to-Image Translation Problems. CoRR Nikita Puchkin, Denis Suchkov, Alexey Naumov, Denis Belomestny
2024	J	`jnl`	A New Bound on the Cumulant Generating Function of Dirichlet Processes. CoRR Pierre Perrault, Denis Belomestny, Pierre Ménard, Éric Moulines, Alexey Naumov, Daniil Tiapkin, Michal Valko
2024	A*	`conf`	Demonstration-Regularized RL. ICLR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
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`	Generative Flow Networks as Entropy-Regularized RL. AISTATS Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov
2024	A*	`conf`	Group and Shuffle: Efficient Structured Orthogonal Parametrization. NeurIPS Mikhail Gorbunov, Nikolay Yudin, Vera Soboleva, Aibek Alanov, Alexey Naumov, Maxim V. Rakhuba
2024	J	`jnl`	Group and Shuffle: Efficient Structured Orthogonal Parametrization. CoRR Mikhail Gorbunov, Nikolay Yudin, Vera Soboleva, Aibek Alanov, Alexey Naumov, Maxim V. Rakhuba
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`	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`	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`	Demonstration-Regularized RL. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	A*	`conf`	Fast Rates for Maximum Entropy Exploration. ICML Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
2023	J	`jnl`	Fast Rates for Maximum Entropy Exploration. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
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	J	`jnl`	Generative Flow Networks as Entropy-Regularized RL. CoRR Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov
2023	A*	`conf`	Model-free Posterior Sampling via Learning Rate Randomization. NeurIPS Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
2023	J	`jnl`	Model-free Posterior Sampling via Learning Rate Randomization. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard
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	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	A*	`conf`	Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. NeurIPS Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
2022	J	`jnl`	Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees. CoRR Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard
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
2021	J	`jnl`	Ex CoRR Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines
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	A*	`conf`	Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise. COLT Maxim Kaledin, Eric Moulines, Alexey Naumov, Vladislav Tadic, Hoi-To Wai
2020	J	`jnl`	Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise. CoRR Maxim Kaledin, Eric Moulines, Alexey Naumov, Vladislav Tadic, Hoi-To Wai
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	J	`jnl`	A FAIR-Based Approach to Enhancing the Discovery and Re-Use of Transcriptomic Data Assets for Nuclear Receptor Signaling Pathways. Data Sci. J. Scott A. Ochsner, Yolanda F. Darlington, Apollo McOwiti, Wasula H. Kankanamge, Alexey Naumov, Lauren B. Becnel, Neil J. McKenna
2017	J	`jnl`	A FAIR-Based Approach to Enhancing the Discovery and Re-Use of Transcriptomic Data Assets for Nuclear Receptor Signaling Pathways. Data Sci. J. Scott A. Ochsner, Yolanda F. Darlington, Apollo McOwiti, Wasula H. Kankanamge, Alexey Naumov, Lauren B. Becnel, Neil J. McKenna
2017	J	`jnl`	Improving the discoverability, accessibility, and citability of omics datasets: a case report. J. Am. Medical Informatics Assoc. Yolanda F. Darlington, Alexey Naumov, Apollo McOwiti, Wasula H. Kankanamge, Lauren B. Becnel, Neil J. McKenna