Adaptive double-exploration tradeoff for outlier detection

Xiaojin Zhang, Honglei Zhuang, Shengyu Zhang, Yuan Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We study a variant of the thresholding bandit problem (TBP) in the context of outlier detection, where the objective is to identify the outliers whose rewards are above a threshold. Distinct from the traditional TBP, the threshold is defined as a function of the rewards of all the arms, which is motivated by the criterion for identifying outliers. The learner needs to explore the rewards of the arms as well as the threshold. We refer to this problem as”double exploration for outlier detection”. We construct an adaptively updated confidence interval for the threshold, based on the estimated value of the threshold in the previous rounds. Furthermore, by automatically trading off exploring the individual arms and exploring the outlier threshold, we provide an efficient algorithm in terms of the sample complexity. Experimental results on both synthetic datasets and real-world datasets demonstrate the efficiency of our algorithm.

Original languageEnglish (US)
Title of host publicationAAAI 2020 - 34th AAAI Conference on Artificial Intelligence
PublisherAmerican Association for Artificial Intelligence (AAAI) Press
Pages6837-6844
Number of pages8
ISBN (Electronic)9781577358350
StatePublished - 2020
Event34th AAAI Conference on Artificial Intelligence, AAAI 2020 - New York, United States
Duration: Feb 7 2020Feb 12 2020

Publication series

NameAAAI 2020 - 34th AAAI Conference on Artificial Intelligence

Conference

Conference34th AAAI Conference on Artificial Intelligence, AAAI 2020
Country/TerritoryUnited States
CityNew York
Period2/7/202/12/20

ASJC Scopus subject areas

  • Artificial Intelligence

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