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Regular version of the site
Book chapter
Stochasticity in Algorithmic Statistics for Polynomial Time

Vereshchagin N., Milovanov A.

In bk.: 32nd Computational Complexity Conference. Вадерн: Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, 2017. P. 1-18.

Working paper
Spatially Adaptive Computation Time for Residual Networks

Figurnov M., Collins M. D., Zhu Y. et al.

arXiv:1612.02297. arXiv. Cornell University, 2016

Research

Research Groups

  Research Groups      Research Seminars      Conferences

Bayesian Group

The Bayesian methods research group is part of HSE’s Faculty of Computer Science and Moscow State University’s Computational Mathematics and Cybernetics Department. The group currently consists of two postgraduates, five Ph.D. students, 10 undergraduate students, and one researcher. Bayesian Group carries out its research on the development of new machine learning and Bayesian inference algorithms, which take into account the specific features of a given problem. In its research projects, the group actively uses the Bayesian framework and, in particular, the theory of graphical models.

Working in the group, students gain experience both in conducting research at a global level and in writing papers for leading international conferences in machine learning and computer vision. Many of the group’s undergraduate and graduate students intern at prominent international universities, as well as the Microsoft and Google corporate research centres. The group also has joint research and educational projects with Yandex, Kaspersky Lab, and the Skolkovo Institute of Science and Technology. Group staff are active in teaching and hold lectures and seminars both in Russian and in English.

Group website

Head of the Group: Dmitry Vetrov