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Regular version of the site

Dean — Ivan Arzhantsev


First Deputy Dean— Tamara Voznesenskaya


Deputy Dean for Research and International Relations - Sergei Obiedkov


Deputy Dean for finance and administration - Irina Plisetskaya


Dean's office

Phone: +7 (495) 772-95-90 * 12332


125319, Moscow, 3 Kochnovsky Proezd (near metro station 'Aeroport'). 

Book chapter
GANs for Biological Image Synthesis

Osokin A., Chessel A., Carazo Salas R. E. et al.

In bk.: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017). Venice: IEEE, 2017. P. 2252-2261.

The Second Term in the Asymptotics for the Number of Points Moving Along a Metric Graph

Vsevolod L. Chernyshev, Tolchennikov A. A.

Regular and Chaotic Dynamics. 2017. Vol. 22. No. 8. P. 937-948.

A Conditional Information Inequality and its Combinatorial Applications

Vereshchagin N., Kaced T., Romashchenko A.

IEEE Transactions on Information Theory. 2018. Vol. 64. No. 5. P. 3610-3615.

Dual subgradient method with averaging for optimal resource allocation
In print

Nesterov Y., Shikhman V.

European Journal of Operational Research. 2018. P. 1-10.

Finite sample properties of the mean occupancy counts and probabilities
In print

Decrouez G. G., Grabchak M., Paris Q.

Bernoulli: a journal of mathematical statistics and probability. 2018. Vol. 24. No. 3. P. 1910-1941.

Open Lectures of the Faculty of Computer Science

Event ended

Faculty of Computer Science will host a series of public lectures on February 20 and 26.


February 20, room 400

17.00 – 17.55. Thibaut Le Gouic (Ecole Centrale de Marseille)

Barycenters in Wasserstein spaces

The Wasserstein barycenter of a set of probabilitycommunity. This talk will introduce some of the problems in machine learning in which this tool appear as a solution. Then, after a formal definition of this notion, we will state its basics properties and study its stability with respect to the set of probability measures of which the barycenter is taken.

19.00 – 19.55. Alexey Naumov (Skoltech/CS HSE)

Large ball probabilities with applications in statistical inference

Many statistical inference problems require estimating the Kolmogorov distance between probabilities of two Gaussian elements to hit a ball in a Hilbert space. In this talk, we derive the bounds which are dimensional-free and depend on the nuclear (Schatten-one) norm of the difference between the covariance operators of the elements. We are also interested in the anti-concentration bound for a squared norm of a non-centered Gaussian element in a Hilbert space. All bounds are sharp and cannot be improved in general. A list of corresponding motivation examples and applications in statistical inference will be provided as well. The talk is based on the recent results arXiv:1708.08663 and arXiv:1703.00871.

February 26, room 317

14.00 – 14.55. Navid Talebanfard (Czech Academy of Sciences)

Hard Instances for SAT algorithms

The Boolean satisfiability problem (SAT) is the problem of deciding whether there exists a truth assignment to a given formula which makes it true. Many non-trivial exponential time algorithms are known. However the precise complexity of SAT remains elusive. Exponential Time Hypothesis (ETH) and Strong ETH are formulated to describe the possible complexities. In this talk I will review some constructions showing that several known classes of algorithms cannot refute Strong ETH. I will then present several open questions in this direction.

15.00 – 15.55. Laurent Beaudou (Université Clermont Auvergne)

Betweenness, Convexity and Lattices
In this lecture, we try to follow a natural progression from the notion of betweenness to that of convexity. Doing so, we explore some de Bruijn-Erdos problems around lines in convexity spaces. We also deal with the use of convex subsets in the framework of lattices, and discuss a potential way to extend Day’s duplication theorem to non-convex sets. This lecture is intended for a broad audience and I will be mostly interested in introducing concepts and open questions around these topics.