Adaptive Double-Exploration Tradeoff for Outlier Detection

by   Xiaojin Zhang, et al.
University of Illinois at Urbana-Champaign
The Chinese University of Hong Kong

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.


Robust Outlier Arm Identification

We study the problem of Robust Outlier Arm Identification (ROAI), where ...

Sequential Outlier Detection based on Incremental Decision Trees

We introduce an online outlier detection algorithm to detect outliers in...

Generic Outlier Detection in Multi-Armed Bandit

In this paper, we study the problem of outlier arm detection in multi-ar...

Exploring k out of Top ρ Fraction of Arms in Stochastic Bandits

This paper studies the problem of identifying any k distinct arms among ...

Thresholding Bandit with Optimal Aggregate Regret

We consider the thresholding bandit problem, whose goal is to find arms ...

REMIX: Automated Exploration for Interactive Outlier Detection

Outlier detection is the identification of points in a dataset that do n...

Outlier Regularization for Vector Data and L21 Norm Robustness

In many real-world applications, data usually contain outliers. One popu...

Please sign up or login with your details

Forgot password? Click here to reset