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      Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens

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          Abstract

          In this paper we propose a multi-armed bandit inspired, pool based active learning algorithm for the problem of binary classification. By carefully constructing an analogy between active learning and multi-armed bandits, we utilize ideas such as lower confidence bounds, and self-concordant regularization from the multi-armed bandit literature to design our proposed algorithm. Our algorithm is a sequential algorithm, which in each round assigns a sampling distribution on the pool, samples one point from this distribution, and queries the oracle for the label of this sampled point. The design of this sampling distribution is also inspired by the analogy between active learning and multi-armed bandits. We show how to derive lower confidence bounds required by our algorithm. Experimental comparisons to previously proposed active learning algorithms show superior performance on some standard UCI datasets.

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          The Nonstochastic Multiarmed Bandit Problem

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            A Sequential Algorithm for Training Text Classifiers

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              An analysis of active learning strategies for sequence labeling tasks

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                Author and article information

                Journal
                26 September 2013
                Article
                1309.6830
                3405961e-1b94-4584-8fc0-1158efe1376f

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                UAI-P-2013-PG-232-241
                Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)
                cs.LG stat.ML
                auai

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