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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

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

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.

Colloquium: Learning on networks of distributions for discrete data. Speaker: Wray Buntine, Monash University

Event ended

I will motivate the talk by reviewing some state of the art models for problems like matrix factorisation models for link prediction and tweet clustering. Then I will review the classes of distributions that can be strung together in networks to generate discrete data. This allows a rich class of models that, in its simplest form, covers things like Poisson matrix factorisation, Latent Dirichlet allocation, and Stochastic block models, but, more generally, covers complex hierarchical models on network and text data. The distributions covered include so-called non-parametric distributions such as the Gamma process. Accompanying these are a set of collapsing and augmentation techniques that are used to generate fast Gibbs samplers for many models in this class. To complete this picture, turning complex network models into fast Gibbs samplers, I will illustrate our recent methods of doing matrix factorisation with side information (e.g., GloVe word embeddings), done for link prediction, for instance, for citation networks.

Venue:
Moscow, Kochnovsky pr.,3, room 317, 18:10 

Everyone interested is welcome to attend.

If you need a pass to HSE, please contact computerscience@hse.ru