Kazakov O.D., Averchenkov A.V., Kulagina N.Yu., Xii International Scientific and Technical Conference Applied Mechanics and Systems Dynamics, Journal of Physics Conference Series, 1210, IOP Publishing Ltd, 2019
A. Kuleshov, A. Bernstein, E. Burnaev, “Kernel regression on manifold valued data”, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA, IEEE, 2018, 120–129
Kuleshov A., Bernstein A., Burnaev E., “Manifold Learning Regression With Non-Stationary Kernels”, Artificial Neural Networks in Pattern Recognition, Annpr 2018, Lecture Notes in Artificial Intelligence, 11081, eds. Pancioni L., Schwenker F., Trentin E., Springer International Publishing Ag, 2018, 152–164
V. Spokoiny, “Penalized maximum likelihood estimation and effective dimension”, Ann. Inst. Henri Poincare-Probab. Stat., 53:1 (2017), 389–429
A. Zaytsev, E. Burnaev, “Large scale variable fidelity surrogate modeling”, Ann. Math. Artif. Intell., 81:1-2 (2017), 167–186
E. Burnaev, I. Panin, B. Sudret, “Efficient design of experiments for sensitivity analysis based on polynomial chaos expansions”, Ann. Math. Artif. Intell., 81:1-2 (2017), 187–207
Rodrigo Rivera, Evgeny Burnaev, 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 2017, 625
E. Burnaev, I. Nazarov, “Conformalized kernel ridge regression”, 2016 15Th IEEE International Conference on Machine Learning and Applications (Icmla 2016), IEEE, 2016, 45–52
M. Belyaev, E. Burnaev, E. Kapushev, M. Panov, P. Prikhodko, D. Vetrov, D. Yarotsky, “Gtapprox: surrogate modeling for industrial design”, Adv. Eng. Softw., 102 (2016), 29–39
A. Zaytsev, “Reliable surrogate modeling of engineering data with more than two levels of fidelity”, Proceedings of 2016 7Th International Conference on Mechanical and Aerospace Engineering, (Icmae), IEEE, 2016, 341–345