Changepoint localization via nested exchangeability tests: from point estimates to confidence intervals
Abstract
We study distribution-free uncertainty quantification for a changepoint estimate in an offline setting. We observe a sequence with a single changepoint and are given an estimate of its location. The pre- and post-change segments are independent and each consists of i.i.d. (or, more generally, exchangeable) observations from arbitrary and unknown distributions. We introduce Changepoint LOcalization via Nested Exchangeability testing (CLONE), a novel method for constructing distribution-free confidence intervals around that estimate by testing exchangeability in nested windows centered at the reference estimate. Under these assumptions, CLONE provides finite-sample coverage for any black-box reference estimator. Using a geometrically shrinking radius grid, CLONE requires only $O(log n)$ permutation tests. We characterize optimal scores for the framework and provide practical recommendations that result in sharp confidence intervals. Under mild assumptions, CLONE confidence intervals have asymptotically vanishing normalized length. Simulations and experiments on image and text data support our theoretical findings.
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BibTeX
@misc{hore2026changepoint,
title = {{Changepoint localization via nested exchangeability tests: from point estimates to confidence intervals}},
author = {Rohan Hore and Aaditya Ramdas},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.07399},
eprint = {2611.07399},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.07399},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Changepoint localization via nested exchangeability tests: from point estimates to confidence intervals
AU - Rohan Hore
AU - Aaditya Ramdas
PY - 2026
DA - 2026/11/05
PB - arXiv
DO - 10.48550/arxiv.2611.07399
UR - https://arxiv.org/abs/2611.07399
AB - We study distribution-free uncertainty quantification for a changepoint estimate in an offline setting. We observe a sequence with a single changepoint and are given an estimate of its location. The pre- and post-change segments are independent and each consists of i.i.d. (or, more generally, exchangeable) observations from arbitrary and unknown distributions. We introduce Changepoint LOcalization via Nested Exchangeability testing (CLONE), a novel method for constructing distribution-free confidence intervals around that estimate by testing exchangeability in nested windows centered at the reference estimate. Under these assumptions, CLONE provides finite-sample coverage for any black-box reference estimator. Using a geometrically shrinking radius grid, CLONE requires only $O(log n)$ permutation tests. We characterize optimal scores for the framework and provide practical recommendations that result in sharp confidence intervals. Under mild assumptions, CLONE confidence intervals have asymptotically vanishing normalized length. Simulations and experiments on image and text data support our theoretical findings.
ER -