Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods

被引：0

作者：

Bogolubsky, Lev ^{[1
,2
]}

Gusev, Gleb ^{[1
,6
]}

Raigorodskii, Andrei ^{[1
,2
,3
,6
]}

Tikhonov, Aleksey ^{[1
]}

Zhukovskii, Maksim ^{[1
,6
]}

Dvurechensky, Pavel ^{[4
,5
]}

Gasnikov, Alexander ^{[5
,6
]}

Nesterov, Yurii ^{[7
,8
]}

机构：

[1] Yandex, Moscow, Russia

[2] Moscow MV Lomonosov State Univ, Moscow, Russia

[3] Buryat State Univ, Ulan Ude, Russia

[4] Weierstrass Inst, Berlin, Germany

[5] Inst Informat Transmiss Problems RAS, Moscow, Russia

[6] Moscow Inst Phys & Technol, Moscow, Russia

[7] Ctr Operat Res & Econometr, Louvain La Neuve, Belgium

[8] Higher Sch Econ, Moscow, Russia

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016) | 2016年 / 29卷

基金：

俄罗斯科学基金会;

关键词：

DERIVATIVES;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we consider a non-convex loss-minimization problem of learning Supervised PageRank models, which can account for features of nodes and edges. We propose gradient-based and random gradient-free methods to solve this problem. Our algorithms are based on the concept of an inexact oracle and unlike the state-of-the-art gradient-based method we manage to provide theoretically the convergence rate guarantees for both of them. Finally, we compare the performance of the proposed optimization methods with the state of the art applied to a ranking task.

引用

页数：9

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