Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2013

Abstract

Conventional learning with expert advice methods assumes a learner is always receiving the outcome (e.g., class labels) of every incoming training instance at the end of each trial. In real applications, acquiring the outcome from oracle can be costly or time consuming. In this paper, we address a new problem of active learning with expert advice, where the outcome of an instance is disclosed only when it is requested by the online learner. Our goal is to learn an accurate prediction model by asking the oracle the number of questions as small as possible. To address this challenge, we propose a framework of active forecasters for online active learning with expert advice, which attempts to extend two regular forecasters, i.e., Exponentially Weighted Average Forecaster and Greedy Forecaster, to tackle the task of active learning with expert advice. We prove that the proposed algorithms satisfy the Hannan consistency under some proper assumptions, and validate the efficacy of our technique by an extensive set of experiments.

Keywords

Accurate prediction, Active Learning, Class labels, Expert advice, Real applications

Discipline

Computer Sciences | Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Ninth Conference UAI 2013: July 12-14, Bellevue, WA

First Page

704

Last Page

713

ISBN

9780974903996

Publisher

AUAI Press

City or Country

Corvallis, OR

Copyright Owner and License

Authors

Additional URL

http://auai.org/uai2013/prints/proceedings.pdf

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