• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site
ФКН
Contacts

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

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

Email: computerscience@hse.ru

 

Administrations

Dean Ivan Arzhantsev

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

Events
Jun 12 – Jun 14
submission: 1 May 2019  
Aug 26 – Aug 30
Registration and Poster Submission deadline — May 1, 2019 
Article
Branching rules related to spherical actions on flag varieties
In press

Roman Avdeev, Petukhov A.

Algebras and Representation Theory. 2019.

Article
Minimax theorems for American options without time-consistency

Belomestny D., Kraetschmer V., Hübner T. et al.

Finance and Stochastics. 2019. Vol. 23. P. 209-238.

Article
Separable discrete functions: Recognition and sufficient conditions

Boros E., Cepek O., Gurvich V.

Discrete Mathematics. 2019. Vol. 342. No. 5. P. 1275-1292.

Article
Cherenkov detectors fast simulation using neural networks

Kazeev N., Derkach D., Ratnikov F. et al.

Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2019.

Book chapter
Averaging Weights Leads to Wider Optima and Better Generalization

Izmailov P., Garipov T., Подоприхин Д. А. et al.

In bk.: Proceedings of the international conference on Uncertainty in Artificial Intelligence (UAI 2018). 2018. P. 876-885.

Faculty Colloquium: On empirical risk minimization and its variants for statistical learning. Speaker: Quentin Paris, HSE

Event ended

January 23, 18:10 – 19:30 

Quentin Paris, HSE 

On empirical risk minimization and its variants for statistical learning

In this talk, we review fundamental principles of empirical risk minimization and its performance guarantees for statistical learning. We discuss the close interaction with the field of empirical processes and the connection to Vapnik–Chervonenkis combinatorics (including the notion of combinatorial dimension). We present the best known theoretical guarantees for the prediction error of empirical risk minimizers, discuss the limitations of the method, and mention some recent contributions.

Colloquium

Venue:

Moscow, Kochnovsky pr.,3, room 205, 18:10 
Everyone interested is welcome to attend. 
If you need a pass to HSE, please contact computerscience@hse.ru