Defensive universal learning with experts

Date

2005

Authors

Poland, Jan
Hutter, Marcus

Journal Title

Journal ISSN

Volume Title

Publisher

Springer Verlag

Abstract

This paper shows how universal learning can be achieved with expert advice. To this aim, we specify an experts algorithm with the following characteristics: (a) it uses only feedback from the actions actually chosen (bandit setup), (b) it can be applied with countably infinite expert classes, and (c) it copes with losses that may grow in time appropriately slowly. We prove loss bounds against an adaptive adversary. From this, we obtain a master algorithm for “reactive” experts problems, which means that the master’s actions may influence the behavior of the adversary. Our algorithm can significantly outperform standard experts algorithms on such problems. Finally, we combine it with a universal expert class. The resulting universal learner performs – in a certain sense – almost as well as any computable strategy, for any online decision problem. We also specify the (worst-case) convergence speed, which is very slow.

Description

Keywords

Citation

Source

Type

Conference paper

Book Title

Algorithmic Learning Theory: 16th International Conference, ALT 2005, Singapore, October 8-11, 2005. Proceedings

Entity type

Access Statement

Open Access

License Rights

DOI

10.1007/11564089_28

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