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Regular version of the site
ФКН
Contacts

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

computerscience@hse.ru

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

Events
Article
First measurement of the CP-violating phase ϕdds in B s 0 → (K+π−)(K−π+) decays

Ratnikov F., Баранов А. С., Borisyak M. A. et al.

Journal of High Energy Physics. 2018. Vol. 2018. P. 1-31.

Book chapter
Efficient Mining of Subsample-Stable Graph Patterns

Buzmakov A. V., Kuznetsov S., Napoli A.

In bk.: 2017 IEEE 17th International Conference on Data Mining (ICDM). New Orleans: IEEE, 2017. Ch. 89. P. 757-762.

Book chapter
Spatially Adaptive Computation Time for Residual Networks

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

In bk.: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017). Curran Associates, Inc., 2017. P. 1039-1048.

Article
Structural Instability in Single-Crystal Rare-Earth Scandium Borates RESc3(BO3)4

Kuz’micheva G. M., Kaurova I. A., Rybakov V. B. et al.

Crystal Growth & Design. 2018. Vol. 18. No. 3. P. 1571-1580.

Colloquium: Perturbed Proximal Gradient Algorithms. Speaker: Eric Moulines (École Polytechnique)

Event ended

February 22, 18:10 – 19:30, room 317

Eric Moulines (École Polytechnique, France) 

Perturbed Proximal Gradient Algorithms

We study a version of the proximal gradient algorithm for which the gradient is intractable and is approximated by Monte Carlo methods (and in particular Markov Chain Monte Carlo). We derive conditions on the step size and the Monte Carlo batch size under which convergence is guaranteed: both increasing batch size and constant batch size are considered. We also derive non-asymptotic bounds for an averaged version. Our results cover both the cases of biased and unbiased Monte Carlo approximation. To support our findings, we discuss the inference of a sparse generalized linear model with random effect and the problem of learning the edge structure and parameters of sparse undirected graphical models.

Venue: Moscow, Kochnovsky proezd, 3, room 317, 18:10

Registration is open. 

Registration