# Distribution-free changepoint localization after sequential change detection

arXiv: https://arxiv.org/abs/2606.01256 · HTML: https://mlsanity.com/paper/2606.01256/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 31 May 2026.

## Builds on (most cited first, 8 of 8)

- [Testing randomness](https://mlsanity.com/paper/1906.09256/) (2019)
- [Testing exchangeability: fork-convexity, supermartingales, and e-processes](https://mlsanity.com/paper/2102.00630/) (2021)
- [Retrain or not retrain: Conformal test martingales for change-point detection](https://mlsanity.com/paper/2102.10439/) (2021)
- [Testing exchangeability by pairwise betting](https://mlsanity.com/paper/2310.14293/) (2023)
- [Offline changepoint localization using a matrix of conformal p-values](https://mlsanity.com/paper/2505.00292/) (2025)
- [Post-detection inference for sequential changepoint localization](https://mlsanity.com/paper/2502.06096/) (2025)
- [Theoretical guarantees for change localization using conformal p-values](https://mlsanity.com/paper/2510.08749/) (2025)
- [Testing For Distribution Shifts with Conditional Conformal Test Martingales](https://mlsanity.com/paper/2602.13848/) (2026)

## Built on by (most cited first, 3 of 3)

- [ARM: Detector-Agnostic Changepoint Attribution with Finite-Sample Error Control](https://mlsanity.com/paper/2608.01691/) (2026)
- [ARC: Augmented-Rank Conformalization for Changepoint Localization --- Finite-Sample Validity and Distribution-Robust Efficiency](https://mlsanity.com/paper/2608.08424/) (2026)
- [Strategic Self-Consistency](https://mlsanity.com/paper/2609.30352/) (2026)

## Abstract (arXiv)

This paper introduces a distribution-free framework for constructing post-detection confidence sets for changepoints after stopping a sequential change detection procedure. It is well known that conformal test martingales can be used to sequentially detect changes in distribution, but by themselves provide no inference for the time at which a proclaimed change occurred. Past work on post-detection inference requires pre- and post-change classes of distributions to be known, but this paper accomplishes localization of the changepoint without any distributional assumptions. We establish finite-sample coverage guarantees (conditional on correct detection). We provide non-asymptotic bounds on the conditional expected size of the confidence sets. Under suitable asymptotic regimes, we prove that the conditional expected size of the confidence set remains uniformly bounded and demonstrate strong empirical performance on simulated and real data. To the best of our knowledge, this is the first general distribution-free framework for sequential changepoint localization with valid post-detection coverage.