We propose a kernel-based online change detection algorithm, running in O(log n) time and space, and show its minimax optimality.

We extend the concept of distribution compression to joint and conditional distributions, introducing algorithms to produce compressed sets that approximate the joint or conditional distribution of a target dataset.