Finding structure in heterogeneous massive datasets is a core task for many statistical learning methods. In this talk, I will present a scalable and efficient compressive graph-based nonparametric method for structural homogeneity tests. Two useful applications of this theory will be discussed in the context of k-sample modeling, and change-point detection.
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