Robust Supervised Learning

Drew Bagnell


  Supervised machine learning techniques developed in the Probably Approximately Correct, Maximum A Posteriori, and Structural Risk Minimiziation frameworks typically make the assumption that the test data a learner is applied to is drawn from the same distribution as the training data. In various prominent applications of learning techniques, from robotics to medical diagnosis to process control, this assumption is violated. We consider a novel framework where a learner may influence the test distribution in a bounded way. From this framework, we derive an efficient algorithm that acts as a wrapper around a broad class of existing supervised learning algorithms while guarranteeing more robust behavior under changes in the input distribution.

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Pradeep Ravikumar
Last modified: Thu Mar 17 11:27:44 EST 2005