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Regret bounds for prediction problems

Geoffrey Gordon
Proceedings of COLT '99, 1999.


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Abstract
We present a unified framework for reasoning about worst-case regret bounds for learning algorithms. This framework is based on the theory of duality of convex functions. It brings together results from computational learning theory and Bayesian statistics, allowing us to derive new proofs of known theorems, new theorems about known algorithms, and new algorithms.

Notes
Associated Lab(s) / Group(s): Auton Lab
Associated Project(s): Auton Project

Text Reference
Geoffrey Gordon, "Regret bounds for prediction problems," Proceedings of COLT '99, 1999.

BibTeX Reference
@inproceedings{Gordon_1999_3257,
   author = "Geoffrey Gordon",
   title = "Regret bounds for prediction problems",
   booktitle = "Proceedings of COLT '99",
   year = "1999",
}

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