Statistics Papers

Document Type

Conference Paper

Date of this Version

2008

Publication Source

Proceedings of the 25th International Conference on Machine Learning

Start Page

440

Last Page

447

Abstract

This paper introduces the Banditron, a variant of the Perceptron [Rosenblatt, 1958], for the multiclass bandit setting. The multiclass bandit setting models a wide range of practical supervised learning applications where the learner only receives partial feedback (referred to as "bandit" feedback, in the spirit of multi-armed bandit models) with respect to the true label (e.g. in many web applications users often only provide positive "click" feedback which does not necessarily fully disclose a true label). The Banditron has the ability to learn in a multiclass classification setting with the "bandit" feedback which only reveals whether or not the prediction made by the algorithm was correct or not (but does not necessarily reveal the true label). We provide (relative) mistake bounds which show how the Banditron enjoys favorable performance, and our experiments demonstrate the practicality of the algorithm. Furthermore, this paper pays close attention to the important special case when the data is linearly separable --- a problem which has been exhaustively studied in the full information setting yet is novel in the bandit setting.

Keywords

Online learning, Bandit algorithms, Perceptron

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Date Posted: 27 November 2017