Prediction with Expert Evaluators’ Advice

  • Alexey Chernov
  • Vladimir Vovk
Conference paper

DOI: 10.1007/978-3-642-04414-4_6

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5809)
Cite this paper as:
Chernov A., Vovk V. (2009) Prediction with Expert Evaluators’ Advice. In: Gavaldà R., Lugosi G., Zeugmann T., Zilles S. (eds) Algorithmic Learning Theory. ALT 2009. Lecture Notes in Computer Science, vol 5809. Springer, Berlin, Heidelberg

Abstract

We introduce a new protocol for prediction with expert advice in which each expert evaluates the learner’s and his own performance using a loss function that may change over time and may be different from the loss functions used by the other experts. The learner’s goal is to perform better or not much worse than each expert, as evaluated by that expert, for all experts simultaneously. If the loss functions used by the experts are all proper scoring rules and all mixable, we show that the defensive forecasting algorithm enjoys the same performance guarantee as that attainable by the Aggregating Algorithm in the standard setting and known to be optimal. This result is also applied to the case of “specialist” experts. In this case, the defensive forecasting algorithm reduces to a simple modification of the Aggregating Algorithm.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alexey Chernov
    • 1
  • Vladimir Vovk
    • 1
  1. 1.Computer Learning Research Centre, Department of Computer ScienceRoyal Holloway, University of LondonEghamUK

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