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

Ravi Ganti, Alexander Gray
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:392-401, 2013.

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 data-sets.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR11-ganti13a, title = {Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens}, author = {Ganti, Ravi and Gray, Alexander}, booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence}, pages = {392--401}, year = {2013}, editor = {Nicholson, Ann and Smyth, Padhraic}, volume = {R11}, series = {Proceedings of Machine Learning Research}, month = {12--14 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r11/main/assets/ganti13a/ganti13a.pdf}, url = {https://proceedings.mlr.press/r11/ganti13a.html}, 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 data-sets.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens %A Ravi Ganti %A Alexander Gray %B Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2013 %E Ann Nicholson %E Padhraic Smyth %F pmlr-vR11-ganti13a %I PMLR %P 392--401 %U https://proceedings.mlr.press/r11/ganti13a.html %V R11 %X 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 data-sets. %Z Reissued by PMLR on 04 October 2026.
APA
Ganti, R. & Gray, A.. (2013). Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R11:392-401 Available from https://proceedings.mlr.press/r11/ganti13a.html. Reissued by PMLR on 04 October 2026.

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