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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 Gergart

 

Dean's office
 

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

computerscience@hse.ru

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

Article
Bias-Corrected Estimation in Continuous Sampling Plans
In print

Decrouez G. G., Robinson A.

Risk Analysis: An International Journal. 2017.

Book chapter
Resource Equivalences in Petri Nets

Lomazova I. A.

In bk.: Application and Theory of Petri Nets and Concurrency. 38th International Conference, PETRI NETS 2017, Zaragoza, Spain, June 25–30, 2017, Proceedings. Vol. 10258: Lecture Notes in Computer Science. Switzerland: Springer International Publishing AG, 2017. P. 19-34.

Book chapter
Some Properties of Antistochastic Strings

Milovanov A.

In bk.: Computer Science -- Theory and Applications 10th International Computer Science Symposium in Russia, CSR 2015. Vol. 9139. Springer, 2015. P. 339-349.

Article
The Euler binary partition function and subdivision schemes

Protasov V. Y.

Mathematics of Computation. 2017. No. 86. P. 1499-1524.

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