14 citations to https://www.mathnet.ru/rus/at6148
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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
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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
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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
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V. Spokoiny, “Penalized maximum likelihood estimation and effective dimension”, Ann. Inst. Henri Poincare-Probab. Stat., 53:1 (2017), 389–429
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A. Zaytsev, E. Burnaev, “Large scale variable fidelity surrogate modeling”, Ann. Math. Artif. Intell., 81:1-2 (2017), 167–186
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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
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Rodrigo Rivera, Evgeny Burnaev, 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 2017, 625
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E. Burnaev, I. Nazarov, “Conformalized kernel ridge regression”, 2016 15Th IEEE International Conference on Machine Learning and Applications (Icmla 2016), IEEE, 2016, 45–52
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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
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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