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Defensive universal learning with experts

Poland, Jan; Hutter, Marcus

Description

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. Prom this, we obtain a master algorithm for "reactive" experts problems, which...[Show more]

CollectionsANU Research Publications
Date published: 2005
Type: Conference paper
URI: http://hdl.handle.net/1885/57751
Source: Algorithmic Learning Theory: Proceedings of the 16th International Conference on Algorithmic Learning Theory (ALT-05) - LNAI 3734
DOI: 10.1007/11564089_28

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