Date of Original Version
Abstract or Description
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 frameworkwhere a learnermay 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.