文章基本信息

标题：Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation
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作者：Yuta Tsuboi ; Hisashi Kashima ; Shohei Hido 等
期刊名称：Information and Media Technologies
电子版ISSN：1881-0896
出版年度：2009
卷号：4
期号：2
页码：529-546
DOI：10.11185/imt.4.529
出版社：Information and Media Technologies Editorial Board
摘要：Covariate shift is a situation in supervised learning where training and test inputs follow different distributions even though the functional relation remains unchanged. A common approach to compensating for the bias caused by covariate shift is to reweight the loss function according to the importance , which is the ratio of test and training densities. We propose a novel method that allows us to directly estimate the importance from samples without going through the hard task of density estimation. An advantage of the proposed method is that the computation time is nearly independent of the number of test input samples, which is highly beneficial in recent applications with large numbers of unlabeled samples. We demonstrate through experiments that the proposed method is computationally more efficient than existing approaches with comparable accuracy. We also describe a promising result for large-scale covariate shift adaptation in a natural language processing task.