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Towards a theory of incentives in machine learning

Towards a theory of incentives in machine learning Towards a Theory of Incentives in Machine Learning ARIEL D. PROCACCIA School of Computer Science and Engineering, The Hebrew University of Jerusalem 1. INTRODUCTION The connection between machine learning and economics is, I feel, quite natural. There is a growing body of work that lies at the intersection of the two elds, but most of this work focuses on applying machine learning paradigms to economic problems. Examples include prediction of consumer behavior [Kalai 2003; Beigman and Vohra 2006], automated design of voting rules [Procaccia et al. 2007; Procaccia et al. 2008], and reduction of mechanism design problems to standard algorithmic questions [Balcan et al. 2005]. Nevertheless, there are preciously few papers investigating the incentives that, in some settings, govern the learning process itself (see, e.g., Perote and PerotePeËœa [2004], Dalvi et al. [2004]); none of them do so in a general machine learning n framework. Where, indeed, do strategic considerations come into play in the learning world? In general, a machine learning algorithm receives a (small but hopefully representative) training set consisting of points sampled from an input space and labeled according to some target function; the algorithm outputs a hypothesis that is presumably close to the target http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGecom Exchanges Association for Computing Machinery

Towards a theory of incentives in machine learning

Procaccia, Arieal D.
ACM SIGecom Exchanges , Volume 7 (2) – Jun 1, 2008
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References (9)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2008 by ACM Inc.
ISSN
1551-9031
DOI
10.1145/1399589.1399595
Publisher site
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Abstract

Towards a Theory of Incentives in Machine Learning ARIEL D. PROCACCIA School of Computer Science and Engineering, The Hebrew University of Jerusalem 1. INTRODUCTION The connection between machine learning and economics is, I feel, quite natural. There is a growing body of work that lies at the intersection of the two elds, but most of this work focuses on applying machine learning paradigms to economic problems. Examples include prediction of consumer behavior [Kalai 2003; Beigman and Vohra 2006], automated design of voting rules [Procaccia et al. 2007; Procaccia et al. 2008], and reduction of mechanism design problems to standard algorithmic questions [Balcan et al. 2005]. Nevertheless, there are preciously few papers investigating the incentives that, in some settings, govern the learning process itself (see, e.g., Perote and PerotePeËœa [2004], Dalvi et al. [2004]); none of them do so in a general machine learning n framework. Where, indeed, do strategic considerations come into play in the learning world? In general, a machine learning algorithm receives a (small but hopefully representative) training set consisting of points sampled from an input space and labeled according to some target function; the algorithm outputs a hypothesis that is presumably close to the target

Journal

ACM SIGecom Exchanges – Association for Computing Machinery

Published: Jun 1, 2008

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