Defensive Universal Learning with Experts
- Cite this paper as:
- Poland J., Hutter M. (2005) Defensive Universal Learning with Experts. In: Jain S., Simon H.U., Tomita E. (eds) Algorithmic Learning Theory. ALT 2005. Lecture Notes in Computer Science, vol 3734. Springer, Berlin, Heidelberg
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.
Unable to display preview. Download preview PDF.