high dimensional probability, statistics, machine learning, random matrices, reinforcement learning
Subject:
high dimensional probability, statistics, machine learning, random matrices, reinforcement learning
Main publications:
Götze F., Naumov A., Spokoiny V., Ulyanov V. V., “Large ball probability, Gaussian comparison and anti-concentration”, Bernoulli, 15:4(A) (2019), 2538-2563
Naumov A., Spokoiny V., Ulyanov V. V., “Bootstrap confidence sets for spectral projectors of sample covariance”, Probability theory and related fields, 174:3-4 (2019), 1091-1132
Goetze F., Naumov A.A., Tikhomirov A., Timushev D., “On the local semicircular law for Wigner ensembles”, Bernoulli, 24:3 (2018), 2358-2400
Kaledin M., Moulines E., Naumov A., Tadic V., Wai H., “Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise”, Proceedings of Thirty Third Conference on Learning Theory, Proceedings of Machine Learning Research, 125, PMLR, 2020, 2144-2203
Tiapkin D., Belomestny D., Moulines E., Naumov A., Samsonov S., Tang Y., Valko M., Menard P., “From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses”, Proceedings of the 39th International Conference on Machine Learning, Proceedings of Machine Learning Research, 162, PMLR, 2022, 21380-21431
Nikita Puchkin, Sergey Samsonov, Denis Belomestny, Eric Moulines, Alexey Naumov, “Rates of convergence for density estimation with generative adversarial networks”, J. Mach. Learn. Res., 25 (2024), 1–47
2.
Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry Vetrov, “Generative flow networks as entropy-regularized RL”, Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS) 2024, Valencia, Spain, Proc. Mach. Learn. Res. (PMLR), 238, 2024, 4213–4221
3.
Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines, “Improved high-probability bounds for the temporal difference learning algorithm via exponential stability”, Proceedings of Thirty Seventh Conference on Learning Theory, Proc. Mach. Learn. Res. (PMLR), 247, 2024, 4511–4547
4.
Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, “Finite-time high-probability bounds for Polyak–Ruppert averaged iterates of linear stochastic approximation”, Math. Oper. Res., 2024, 1–39 (Published online) , arXiv: 2207.04475
5.
A. A. Masyutin, A. V. Savchenko, A. A. Naumov, S. V. Samsonov, D. N. Tyapkin, D. V. Belomestny, D. S. Morozova, D. A. Bad'ina, “Development of applied solutions based on artificial intelligence for technological security control”, Dokl. Math., 106:suppl. 1 (2022), S23–S27
6.
F. Götze, A. A. Naumov, A. N. Tikhomirov, “Moment Inequalities for Linear and Nonlinear Statistics”, Theory Probab. Appl., 65:1 (2020), 1–16
7.
F. Götze, A. A. Naumov, A. N. Tikhomirov, “Local semicircle law under moment conditions: Stieltjes transform, rigidity and delocalization”, Theory Probab. Appl., 62:1 (2018), 58–83
8.
F. Götze, A. A. Naumov, A. N. Tikhomirov, “Limit theorems for two classes of random matrices with dependent entries”, Theory Probab. Appl., 59:1 (2015), 23–39
9.
A. A. Naumov, “Limit theorems for two classes of random matrices with Gaussian elements”, J. Math. Sci. (N. Y.), 204:1 (2015), 140–147
10.
A. A. Naumov, Teor. Veroyatnost. i Primenen., 55:3 (2010), 616–617
Proceedings
11.
D. Tiapkin, D. Belomestny, D. Calandriello, E. Moulines, A. Naumov, P. Perrault, M. Valko, P. Menard, “Demonstration-regularized RL”, International Conference on Learning Representations, 2024, 1–65https://openreview.net/forum?id=lF2aip4Scn, arXiv: 2310.17303
12.
M. Gorbunov, N. Yudin, V. Soboleva, A. Alanov, A. Naumov, M. Rakhuba, “Group and Shuffle: Efficient Structured Orthogonal Parametrization”, 38th Conference on Neural Information Processing Systems (NeurIPS 2024), Neural Information Processing Systems, NeurIPS, 2024, 1–27https://openreview.net/pdf?id=7EQx56YSB2
13.
S. Samsonov, E. Moulines, Q. M. Shao, Z. S. Zhang, A. Naumov, “Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning”, 38th Conference on Neural Information Processing Systems (NeurIPS 2024), Neural Information Processing Systems, NeurIPS, 2024, 1–53https://openreview.net/pdf?id=S0Ci1AsJL5; ; ; ;
On Polynomials in Random Elements V. V. Ulyanov, A. A. Naumov International Scientific Conference "Probability Theory and its Applications" On Occasion of 85th Birthday of Yu. V. Prokhorov February 14, 2015 17:15
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