Testing Exchangeability On-Line

Vladimir Vovk, Ilia Nouretdinov, and Alex Gammerman

The majority of theoretical work in machine learning is done under the assumption of exchangeability: essentially, it is assumed that the examples are generated from the same probability distribution independently. This paper is concerned with the problem of testing the exchangeability assumption in the on-line mode: examples are observed one by one and the goal is to monitor on-line the strength of evidence against the hypothesis of exchangeability. We introduce the notion of exchangeability martingales, which are online procedures for detecting deviations from exchangeability; in essence, they are betting schemes that never risk bankruptcy and are fair under the hypothesis of exchangeability. Some specific exchangeability martingales are constructed using Transductive Confidence Machine. We report experimental results showing their performance on the USPS benchmark data set of hand-written digits (known to be somewhat heterogeneous); one of them multiplies the initial capital by more than 1018; this means that the hypothesis of exchangeability is rejected at the significance level 10-18.

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