Семинар HDI Lab: доклады Błażej Miasojedow (Варшавский университет) и Eric Moulines (Ecole Polytechnique, НИУ ВШЭ)

в 17:00 - Błażej Miasojedow (Варшавский университет), доклад "Statistical inference for Continous Time Bayesian Networks" 

Аннотация:
Continuous Time Bayesian Networks (CTBN) are a class of multivariate Markov Jump Processes (MJP),where dependence between coordinates is described by a directed graph with possible cycles. CTBNs are a flexible tool for modelling phenomena from different areas such as chemistry, biology, social sciences etc. In the talk, I will discuss the problems of parameter learning and structure learning for CTBNs with a special attention to computational methods. 

в 18:00 - Eric Moulines (Ecole Polytechnique, HSE), доклад "Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames"

Аннотация:
Many applications of machine learning involve the analysis of large data frames - matrices collecting heterogeneous measurements (binary, numerical, counts, etc.) across samples -- with missing values. Low-rank models, as studied by Udell et al. (2016), are popular in this framework for tasks such as visualization, clustering and missing value imputation. Yet, available methods with statistical guarantees and efficient optimization do not allow explicit modeling of main additive effects such as row and column, or covariate effects. In this paper, we introduce a low-rank interaction and sparse additive effects (LORIS) model which combines matrix regression on a dictionary and low-rank design, to estimate main effects and interactions simultaneously. We provide statistical guarantees in the form of upper bounds on the estimation error of both components. Then, we introduce a mixed coordinate gradient descent (MCGD) method which provably converges sub-linearly to an optimal solution and is computationally efficient for large scale data sets. We show on simulated and survey data that the method has a clear advantage over current practices.

Заказ пропуска: vkuznecova@hse.ru