
ВИДЕОТЕКА 
Workshop “Frontiers of High Dimensional Statistics, Optimization, and Econometrics”



[Random gradientfree methods for random walk based web page ranking functions learning] П. Е. Двуреченский^{} ^{} Московский физикотехнический институт (государственный университет), г. Долгопрудный Московской обл. 

Аннотация: In this talk we consider a problem of web page relevance to a search query. We are working in the framework called SemiSupervised PageRank which can account for some properties which are not considered by classical approaches such as PageRank and BrowseRank algorithms. We introduce a graphical parametric model for web pages ranking. The goal is to identify the unknown parameters using the information about page relevance to a number of queries given by some experts (assessors). The resulting problem is formulated as an optimization one. Due to hidden huge dimension of the last problem we develop random gradientfree methods with oracle error to solve it. We prove the convergence theorem and give the number of arithmetic operations which is needed to solve it with a given accuracy. This is a joint work with A. Gasnikov and M. Zhukovskii. Язык доклада: английский 