Plug-in martingales for testing exchangeability on-line

Research output: Chapter in Book/Report/Conference proceedingConference contribution


A standard assumption in machine learning is the exchangeability of data, which is equivalent to assuming that the examples are generated from the same probability distribution independently. This paper is devoted to testing the assumption of exchangeability on-line: the examples arrive one by one, and after receiving each example we would like to have a valid measure of the degree to which the assumption of exchangeability has been falsified. Such measures are provided by exchangeability martingales. We extend known techniques for constructing exchangeability martingales and show that our new method is competitive with the martingales introduced before. Finally we investigate the performance of our testing method on two benchmark datasets, USPS and Statlog Satellite data; for the former, the known techniques give satisfactory results, but for the latter our new more flexible method becomes necessary.
Original languageEnglish
Title of host publicationProceedings of the 29th International Conference on Machine Learning (ICML-12)
EditorsJohn Langford, Joelle Pineau
Number of pages8
ISBN (Print)978-1-4503-1285-1
Publication statusPublished - 2012

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