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Regular version of the site

109028, Moscow,
11, Pokrovsky boulevard

Phone: +7 (495) 531-00-00 *27254

Email: computerscience@hse.ru


First Deputy Dean Tamara Voznesenskaya
Deputy Dean for Research and International Relations Sergei Obiedkov
Deputy Dean for Methodical and Educational Work Ilya Samonenko
Deputy Dean for Development, Finance and Administration Irina Plisetskaya
Convergence rates for empirical barycenters in metric spaces: curvature, convexity and extendable geodesics

Ahidar-Coutrix A., Le Gouic T., Paris Q.

Probability Theory and Related Fields. 2019.

Machine Learning on data with sPlot background subtraction

M. Borisyak, N. Kazeev.

Journal of Instrumentation. 2019. Vol. 14. No. 08. P. 1-8.

Book chapter
Subspace Inference for Bayesian Deep Learning

Vetrov D., Izmailov P., Maddox W. J. et al.

In bk.: Proceedings of the 35th Uncertainty in Artificial Intelligence Conference (UAI-2019). 2019. P. 1-11.

Book chapter
The logic of action lattices is undecidable

Kuznetsov S.

In bk.: 34th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS 2019). IEEE, 2019. Ch. 36. P. 1-9.

HSE-Yandex autumn school on generative models

*recommended age
Event ended

HSE University and Yandex invite you to joint autumn school on generative models aimed at undergraduate/graduate students and young postdoctoral fellows from pure and applied mathematics. This intense four-day workshop will consist of 3 interdisciplinary mini-courses, list of invited talks, poster session by the participants and master-classes by industrial partners. Key topics of this school:

  • Generative Adversarial Nets
  • Statistical and Computational Optimal Transport
  • Bayesian methods in machine learning

This school is supported by the RSF grant N19-71-30020 "Applications of probabilistic artificial neural generative models to development of digital twin technology for Non-linear stochastic systems" and organised by three laboratories of HSE University:

  • LAMBDA (Laboratory of Methods for Big Data Analysis)
  • DeepBayes (Centre of Deep Learning and Bayesian Methods)
  • HDI Lab (International laboratory of stochastic algorithms and high-dimensional inference)

Yandex is an industry partner of the school.


Invited talks:

  • Artem Babenko (Yandex, HSE)
  • Evgeny Burnaev (Skoltech)
  • Viktor Lempitsky (Skoltech)
  • Ivan Oseledets (Skoltech)
  • Maxim Panov (Skoltech, HSE)
  • Vladimir Spokoiny (WIAS, HSE)
  • TBC

Poster Submission Guidelines:

Only posters of submitted and accepted abstracts will be offered presentation. Please note the following information for the preparation of your poster. Please bring your printed poster with you to the Conference.

Poster Preparation

  • The poster layout is PORTRAIT.
  • Please prepare your poster to fit the dimensions below. The poster can be prepared either on one sheet or few sheets of paper.
  • The dimensions of the poster should not exceed 59.4 cm wide x 84 cm long (23.4 inches wide x 33.1 inches long). The recommended but not mandatory format is A1.
  • Allocate the top of the poster for the title and authors as stated on the submitted abstract.
  • Pins will be available for the mounting of posters.

Registration and more information

 26 Nov 2019, 09:00 
Ends 29 Nov 2019, 19:00

Location: Yandex, Moscow
Moscow, 119021, 16, Ulitsa Lva Tolstogo
room "Princeton"


Ivan Arzhantsev
Dean: Faculty of computer science

Alexey Naumov
Head of International laboratory of Stochastic Algorithms and High-dimensional inference

Dmitry Vetrov
Head of the centre of Deep learning and Bayesian methods

Andrey Ustyuzhanin 
Head of laboratory of Methods for Big Data analysis