1. D. Belomestny, L. Iosipoi, N. Zhivotovskiy, Variance reduction via empirical variance minimization: convergence and complexity, (ArXiv: 1712.04667).
  2. A. Kupavskii, N. Zhivotovskiy, When are epsilon-nets small? (ArXiv: 1711.10414).
  3. N. Zhivotovskiy, Optimal learning via local entropies and sample compression (ArXiv: 1706.01124).
  4. I. Silin, V. Spokoiny, Bayesian inference for spectral projectors of covariance matrix, (ArXiv: 1711.11532).
  5. I. Silin, Finite sample Bernstein – von Mises theorems for functionals and spectral projectors of covariance matrix, (ArXiv: 1712.03522).
  6. K. Efimov, L. Adamyan, V. Spokoiny, Adaptive Nonparametric Clustering (ArXiv: 1709.09102).
  7. F. Goetze, A. Naumov, A. Tikhomirov, On Local laws for non-Hermitian random matrices and their products, (ArXiv: 1708.06950).
  8. F. Goetze, A. Naumov, V. Spokoiny, V. Ulyanov, Gaussian comparison and anti-concentration inequalities for norms of Gaussian random elements, (ArXiv: 1708.08663).
  9. A. Naumov, V. Spokoiny, V. Ulyanov, Bootstrap confidence sets for spectral projectors of sample covariances, 2017 (ArXiv: 1703.00871).
  10. M. Panov, K. Slavnov, R. Ushakov, Consistent Estimation of Mixed Memberships with Successive Projections, Proceedings of Complex Networks 2017 (The 6th International Conference on Complex Networks and Their Applications) (arXiv:1707.01350).
  11. N. Mokrov, M. Panov, Simultaneous Matrix Diagonalization for Structural Brain Networks Classification, Proceedings of Complex Networks 2017 (The 6th International Conference on Complex Networks and Their Applications) (arXiv:1710.05213).
  12. D. Ermilov, M. Panov, Y. Yanovich, Automatic Bitcoin Address Clustering, International Conference of Machine Learning and Applications, 2017.
  13. K. Slavnov, M. Panov, Overlapping Community Detection in Weighted Graphs: Matrix Factorization Approach, Proceedings of IIP conference, Springer, 2017.
  1.  F. Goetze, A. Naumov, A. Tikhomirov, D. Timushev,  On the local semicircle law for Wigner ensembles, Bernoulli, 24(3), 2358-2400, 2018 (ArXiv:1602.03073).
  1. F. Goetze, A. Naumov, A. Tikhomirov, Local semicircle law under moment conditions: Stieltjes transform, rigidity and delocalization, Theory Probab. Appl., 62 (1), 72-103, 2017 (ArXiv:1510.07350 and ArXiv:1511.00862).
  2. F. Goetze, A. Naumov, V. Ulyanov, Asymptotic analysis of symmetric functions, Journal of Theoretical Probability, 30 (3), 876–897, 2017 (ArXiv: 1502.0626).
  3. F. Goetze, A. Naumov, A. Tikhomirov, Distribution of linear statistics of singular values of the product of random matrices, Bernoulli, 23 (4B), 3067-3113, 2017 (ArXiv: 1412:3314).
  1. M. Belyaev, E. Burnaev, E. Kapushev, M. Panov, P. Prikhodko, D. Vetrov, D. Yarotsky. GTApprox: Surrogate Modeling for Industrial Design. Advances in Engineering Software. — 2016. — Vol. 102. — Pp. 29–39.
  2. M. Panov. Nonasymptotic approach to Bayesian semiparametric inference. Doklady Mathematics, 93 (2), pp. 155-158.
  3. E. Burnaev, A. Zaytsev, M. Panov. Regression on the Basis Nonstationary Gaussian Processes with Bayesian Regularization. Journal of Communications Technology and Electronics, 61(6), pp 661-671.
  4. A. Naumov, A. Tikhomirov, Local Laws for Random Matrices and Random Graphs, in proceeding of ITAS, 2016.
  5. F. Goetze, A. Naumov, A. Tikhomirov and D. Timushev. Local Semicircle Law under Weak Moment Conditions. Doklady Mathematics, 93 (3), 1–3, 2016.
  6. S. Hanneke, N. Zhivotovskiy. Localization of VC Classes: Beyond Local Rademacher Complexities. To appear in Theoretical Computer Science. Short conference version in Algorithmic Learning Theory, LNCS, arXiv:1606.00922.
  1. M. Panov, V. Spokoiny. Finite Sample Bernstein – von Mises Theorem for Semiparametric Problems. Bayesian analysis, 10(3), pp. 665–710.
  2. E. Burnaev, M. Panov. Adaptive Design of Experiments based on Gaussian Processes. Proceedings of Symposium on Statistical Learning and Data Sciences, Lecture notes in computer science, pp. 116-125.
  3. F. Goetze, A. Naumov, A. Tikhomirov, On a generalization of the elliptic law for random matrices, Acta Phys. Polon. B, 46 (9), 1737–1745, 2015 (see also ArXiv: 1404:7013).

  4. F. Goetze, A. Naumov, A. Tikhomirov, On minimal singular values of random matrices with correlated entries, Random Matrices Theory Appl., 4 (2), 1550006, 30, 2015 (see also ArXiv:1309:5711).

  5. F. Goetze, A. Naumov, A. Tikhomirov, Limit theorems for two classes of random matrices with dependent entries. Theory Probab. Appl., 59 (114)(1):23-39, 2015 (see also ArXiv: 1211.0389).

  6. N. Zhivotovskiy. Combinatorial Bounds of Overfitting with Sub-logarithmic Order of Growth. Proceedings of MIPT, pp. 42 – 54.
  7. G. Blanchard, I. Tolstikhin, N. Zhivotovskiy. Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning. Algorithmic Learning Theory, Lecture Notes in Computer Science, arXiv:1505.02910.
  1. M. Panov, V. Spokoiny. Critical Dimension in Semiparametric Bernstein – von Mises Theorem. Proceedings of Steklov mathematical Institute, 287, pp. 242–266.
  2. A. Naumov. Limit theorems for two classes of random matrices with Gaussian elements. Journal of Mathematical Sciences, 204(1):140–147, 2014.
  1. A. Naumov. Elliptic law for random matrices. Vestnik Moskov. Univ. Ser. XV Vychisl. Mat. Kibernet., (1):31–38, 2013. (see also Elliptic law for real random matrices, ArXiv: 1201.1639)
  1. M. Panov, A. Tatarchuk, V. Mottl, D. Windridge. A Modified Neutral Point Method for Kernel-based Fusion of Pattern-Recognition Modalities with Incomplete Data Sets. Proceedings of the 10th International Workshop MCS 2011, Naples, Italy, June 15-17, 2011. Proceedings. Springer Berlin Heidelberg, 2011. P. 126-136.