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 PerotePeËa [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
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Towards a theory of incentives in machine learning
Procaccia, Arieal D.
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, Volume 7 (2)
Association for Computing Machinery – Jun 1, 2008
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