Burnaev E.V., “Algorithmic Foundations of Predictive Analytics in Industrial Engineering Design”, J. Commun. Technol. Electron., 64:12 (2019), 1485–1492
Evgenii Egorov, Kirill Neklydov, Ruslan Kostoev, Evgeny Burnaev, Lecture Notes in Computer Science, 11554, Advances in Neural Networks – ISNN 2019, 2019, 409
A. Kuleshov, A. Bernstein, E. Burnaev, “Kernel regression on manifold valued data”, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (Dsaa), Proceedings of the International Conference on Data Science and Advanced Analytics, 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
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
E. V. Burnaev, M. E. Panov, A. A. Zaytsev, “Regression on the basis of nonstationary Gaussian processes with Bayesian regularization”, J. Commun. Technol. Electron., 61:6 (2016), 661–671
E. Burnaev, I. Nazarov, “Conformalized kernel ridge regression”, 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, Lecture Notes in Computer Science, 9653, Conformal and Probabilistic Prediction with Applications, 2016, 147