Mikhail Belyaev

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We consider the problem of multidimensional approximation according to samples with a factorial experimental design (full or incomplete). Universal approximation methods do not take this peculiarity of a sample into account. In the present work, a structural approximation method is developed: the function class and the regularization are chosen in a special(More)
In this paper, we tackle a problem of predicting phenotypes from structural connectomes. We propose that normalized Laplacian spectra can capture structural properties of brain networks, and hence graph spectral distributions are useful for a task of connectome-based classification. We introduce a kernel that is based on earth mover's distance (EMD) between(More)
We describe GTApprox — a new tool for medium-scale surrogate modeling in industrial design. Compared to existing software, GTApprox brings several innovations: a few novel approximation algorithms, several advanced methods of automated model selection, novel options in the form of hints. We demonstrate the efficiency of GTApprox on a large collection of(More)
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