Robust Supervised Learning

J. Andrew (Drew) Bagnell
Proceedings of AAAI, June, 2005.

  • Adobe portable document format (pdf) (118KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Supervised machine learning techniques developed in the Probably Approximately Correct, Maximum A Posteriori, and Structural Risk Minimization 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, this assumption is violated. We consider a novel framework where a learner 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 techniques while guarranteeing more robst behavior under changes in input distribution.

Associated Center(s) / Consortia: National Robotics Engineering Center
Number of pages: 6

Text Reference
J. Andrew (Drew) Bagnell, "Robust Supervised Learning," Proceedings of AAAI, June, 2005.

BibTeX Reference
   author = "J. Andrew (Drew) Bagnell",
   title = "Robust Supervised Learning",
   booktitle = "Proceedings of AAAI",
   publisher = "American Association for Artifical Intelligence",
   month = "June",
   year = "2005",