Learning the experts for online sequence prediction

Elad Eban*, Aharon Birnbaum, Shai Shalev-Shwartz, Amir Globerson

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


Online sequence prediction is the problem of predicting the next element of a sequence given previous elements. This problem has been extensively studied in the context of individual sequence prediction, where no prior assumptions are made on the origin of the sequence. Individual sequence prediction algorithms work quite well for long sequences, where the algorithm has enough time to learn the temporal structure of the sequence. However, they might give poor predictions for short sequences. A possible remedy is to rely on the general model of prediction with expert advice, where the learner has access to a set of r experts, each of which makes its own predictions on the sequence. It is well known that it is possible to predict almost as well as the best expert if the sequence length is order of log(r). But, without firm prior knowledge on the problem, it is not clear how to choose a small set of good experts. In this paper we describe and analyze a new algorithm that learns a good set of experts using a training set of previously observed sequences. We demonstrate the merits of our approach by applying it on the task of click prediction on the web.

Original languageAmerican English
Title of host publicationProceedings of the 29th International Conference on Machine Learning, ICML 2012
Number of pages8
StatePublished - 2012
Event29th International Conference on Machine Learning, ICML 2012 - Edinburgh, United Kingdom
Duration: 26 Jun 20121 Jul 2012

Publication series

NameProceedings of the 29th International Conference on Machine Learning, ICML 2012


Conference29th International Conference on Machine Learning, ICML 2012
Country/TerritoryUnited Kingdom


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