Human Boosting

Harsh Pareek, Pradeep Ravikumar

Abstract:   Humans may be exceptional learners but they have biological limitations and moreover, inductive biases similar to machine learning algorithms. This puts limits on human learning ability and on the kinds of learning tasks humans can easily handle. In this paper, we consider the problem of boosting” human learners to extend the learning ability of human learners and achieve improved performance on tasks which individual humans nd dicult. We consider classi cation (category learning) tasks, propose a boosting algorithm for human learners and give theoretical justi cations. We conduct experiments using Amazon’s Mechanical Turk on two synthetic datasets { a crosshair task with a nonlinear decision boundary and a gabor patch task with a linear boundary but which is inaccessible to human learners { and one real world dataset { the Opinion Spam detection task introduced in (Ott et al., 2011). Our results show that boosting human learners produces gains in accuracy and can overcome some fundamental limitations of human learners.

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  • Human Boosting (pdf)
    H. Pareek, P. Ravikumar.
    In International Conference on Machine Learning (ICML), 2013.

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