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Alexey Naumov
Alexey Naumov
Professor, HSE University
Zweryfikowany adres z hse.ru - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Finite time analysis of linear two-timescale stochastic approximation with Markovian noise
M Kaledin, E Moulines, A Naumov, V Tadic, HT Wai
Conference on Learning Theory, 2144-2203, 2020
912020
Large ball probabilities, Gaussian comparison and anti-concentration
F Götze, A Naumov, V Spokoiny, V Ulyanov
Bernoulli 25 (4A), 2538-2563, 2019
622019
Limit theorems for two classes of random matrices with dependent entries
F Götze, AA Naumov, AN Tikhomirov
Theory of Probability & Its Applications 59 (1), 23-39, 2015
58*2015
Bootstrap confidence sets for spectral projectors of sample covariance
A Naumov, V Spokoiny, V Ulyanov
Probability Theory and Related Fields 174 (3), 1091-1132, 2019
522019
Elliptic law for real random matrices
A Naumov
arXiv preprint arXiv:1201.1639, 2012
462012
On the local semicircular law for Wigner ensembles
F Götze, A Naumov, A Tikhomirov, D Timushev
Bernoulli 24 (3), 2358-2400, 2018
382018
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
A Durmus, E Moulines, A Naumov, S Samsonov, K Scaman, HT Wai
Advances in Neural Information Processing Systems 35, 2021
282021
Variance reduction for Markov chains with application to MCMC
D Belomestny, L Iosipoi, E Moulines, A Naumov, S Samsonov
Statistics and Computing 30, 973-997, 2020
282020
Generative flow networks as entropy-regularized rl
D Tiapkin, N Morozov, A Naumov, DP Vetrov
International Conference on Artificial Intelligence and Statistics, 4213-4221, 2024
272024
Local semicircle law under moment conditions: The stieltjes transform, rigidity, and delocalization
F Götze, AA Naumov, AN Tikhomirov
Theory of Probability & Its Applications 62 (1), 58-83, 2018
26*2018
Finite-time high-probability bounds for Polyak–Ruppert averaged iterates of linear stochastic approximation
A Durmus, E Moulines, A Naumov, S Samsonov
Mathematics of Operations Research, 2024
242024
Local-Global MCMC kernels: the best of both worlds
S Samsonov, E Lagutin, M Gabrié, A Durmus, A Naumov, E Moulines
Advances in Neural Information Processing Systems 36, 2022
242022
On the stability of random matrix product with markovian noise: Application to linear stochastic approximation and td learning
A Durmus, E Moulines, A Naumov, S Samsonov, HT Wai
Conference on Learning Theory 134, 1711-1752, 2021
242021
Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations
D Belomestny, A Naumov, N Puchkin, S Samsonov
Neural Networks 161, 242-253, 2023
232023
Fast Rates for Maximum Entropy Exploration
D Tiapkin, D Belomestny, D Calandriello, E Moulines, R Munos, ...
International Conference on Machine Learning, 2023
212023
Rates of convergence for density estimation with generative adversarial networks
N Puchkin, S Samsonov, D Belomestny, E Moulines, A Naumov
Journal of Machine Learning Research 25 (29), 1-47, 2024
20*2024
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
A Beznosikov, S Samsonov, M Sheshukova, A Gasnikov, A Naumov, ...
Advances in Neural Information Processing Systems 37, 2023
202023
From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses
D Tiapkin, D Belomestny, E Moulines, A Naumov, S Samsonov, Y Tang, ...
International Conference on Machine Learning 162, 21380-21431, 2022
202022
Distribution of linear statistics of singular values of the product of random matrices
F Götze, A Naumov, A Tikhomirov
Bernoulli 23 (4B), 3067-3113, 2017
192017
On minimal singular values of random matrices with correlated entries
F Götze, A Naumov, A Tikhomirov
Random Matrices: Theory and Applications 4 (02), 1550006, 2015
192015
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