Title: Conformal changepoint localization Authors:  Aaditya Ramdas - Carnegie Mellon University (United States) [presenting]
Abstract: Offline changepoint localization is the problem of estimating the index at which a change occurred in the data-generating distribution of an ordered list of data. We present the broadly applicable CONCH (CONformal CHangepoint localization) algorithm, which uses a matrix of conformal p-values to produce a confidence interval for a changepoint under the mild assumption that the pre-change and post-change distributions are each exchangeable. We exemplify the CONCH algorithm on a variety of synthetic and real-world datasets, including using black-box classifiers to detect changes in sequences of images or text.