1. On the Detection of Concept Changes in
Time-Varying Data Stream by Testing Exchangeability
Shen-Shyang Ho and Harry Wechsler
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Department of Computer Science
George Mason University
Problem: In a data streaming setting, data points are
observed one by one. The concepts to be learned from
the data stream may change infinitely often.
How do we detect the changes efficiently?

2. A martingale framework was proposed and two tests are suggested [Ho, ICML 2005]
Show that the test (MT1) based on Doob's Inequality is an approximation to the sequential probability ratio test (SPRT).
Relationship between the threshold value with respect to the size and power of the test is established
Mean delay time before a change being detected is estimated.
Test (MT2) based on the Hoeffding-Azuma Inequality.
Under some assumptions, MT2 has a lower false positive (i.e. Declaring a change when in fact there is no change) rate than MT1.