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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

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

Article
Colourings of Uniform Hypergraphs with Large Girth and Applications

Shabanov D. S., Kupavskii A.

Combinatorics Probability and Computing. 2017.

Book chapter
A Fast Scaling Algorithm for the Weighted Triangle-Free 2-Matching Problem

Artamonov S., Babenko M. A.

In bk.: Combinatorial Algorithms. 26th International Workshop, IWOCA 2015, Verona, Italy, October 5-7, 2015, Revised Selected Papers. Vol. 9538. Switzerland: Springer International Publishing, 2016. P. 25-37.

Book chapter
Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm

Yakovlev K., Andreychuk A.

In bk.: Proceedings of the 27th International Conference on Automated Planning and Scheduling (ICAPS 2017). Palo Alto: AAAI Press, 2017. P. 586-594.

Article
Cohomology of toric origami manifolds with acyclic proper faces

Ayzenberg A., Masuda M., Park S. et al.

Journal of Symplectic Geometry. 2017. Vol. 15. No. 3. P. 645-685.

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