Offline changepoint localization using a matrix of conformal p-values
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914337859305472 |
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| author | Dandapanthula, Sanjit Ramdas, Aaditya |
| author_facet | Dandapanthula, Sanjit Ramdas, Aaditya |
| contents | 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, or declaring that no change occurred. We present the broadly applicable MCP algorithm, which uses a matrix of conformal p-values to produce a confidence interval for a (single) changepoint under the mild assumption that the pre-change and post-change distributions are each exchangeable. We prove a novel conformal Neyman-Pearson lemma, motivating practical classifier-based choices for our conformal score function. Finally, we exemplify the MCP algorithm on a variety of synthetic and real-world datasets, including using black-box pre-trained classifiers to detect changes in sequences of images, text, and accelerometer data. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_00292 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Offline changepoint localization using a matrix of conformal p-values Dandapanthula, Sanjit Ramdas, Aaditya Statistics Theory Signal Processing Methodology 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, or declaring that no change occurred. We present the broadly applicable MCP algorithm, which uses a matrix of conformal p-values to produce a confidence interval for a (single) changepoint under the mild assumption that the pre-change and post-change distributions are each exchangeable. We prove a novel conformal Neyman-Pearson lemma, motivating practical classifier-based choices for our conformal score function. Finally, we exemplify the MCP algorithm on a variety of synthetic and real-world datasets, including using black-box pre-trained classifiers to detect changes in sequences of images, text, and accelerometer data. |
| title | Offline changepoint localization using a matrix of conformal p-values |
| topic | Statistics Theory Signal Processing Methodology |
| url | https://arxiv.org/abs/2505.00292 |