Abstract
We consider the problem of converting offline estimators into an online predictor or estimator with small extra regret. Formally this is the problem of merging a collection of probability measures over strings of length 1,2,3,... into a single probability measure over infinite sequences. We describe various approaches and their pros and cons on various examples. As a side-result we give an elementary non-heuristic purely combinatoric derivation of Turing’s famous estimator. Our main technical contribution is to determine the computational complexity of online estimators with good guarantees in general.
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Hutter, M. (2014). Offline to Online Conversion. In: Auer, P., Clark, A., Zeugmann, T., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2014. Lecture Notes in Computer Science(), vol 8776. Springer, Cham. https://doi.org/10.1007/978-3-319-11662-4_17
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DOI: https://doi.org/10.1007/978-3-319-11662-4_17
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11661-7
Online ISBN: 978-3-319-11662-4
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