arXiv:2609. 08234v1 Announce Type: cross Abstract: Suppose we are given an ordered sequence of independent data whose distribution changes $K$ times at unknown locations, for some unknown $K \geq 0$.
arXiv:2606. 01256v1 Announce Type: cross Abstract: This paper introduces a distribution-free framework for constructing post-detection confidence sets for changepoints after stopping a sequential change detection procedure.
arXiv:2609.27179v1 Announce Type: cross Abstract: We study distribution-free sequential changepoint detection for independent observations with unknown and unrestricted pre- and post-change laws. We...
arXiv:2511. 04275v2 Announce Type: replace-cross Abstract: Conformal prediction has emerged as a powerful framework for constructing distribution-free prediction sets with guaranteed coverage assuming only the exchangeability assumption.
arXiv:2605. 20726v2 Announce Type: replace-cross Abstract: Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting test samples whose conformal p-values fall below a threshold.
arXiv:2609. 11918v1 Announce Type: new Abstract: Generalization under distribution shift remains a core challenge in modern machine learning, yet existing learning bound theory is limited to narrow, idealized settings and is non-estimable from samples.
arXiv:2607. 27301v1 Announce Type: cross Abstract: Isotonic regression is a canonical tool for estimating monotone functions and calibrating probabilistic predictors.
arXiv:2602. 13848v2 Announce Type: replace Abstract: We propose a sequential test for detecting arbitrary distribution shifts that allows conformal test martingales (CTMs) to work under a fixed, reference-conditional setting.
arXiv:2504. 19952v2 Announce Type: replace-cross Abstract: We present two general lower bounds for stopping times of sequential tests between arbitrary composite nulls $\mathcal P$ and alternatives $\mathcal Q$.
arXiv:2606. 03600v1 Announce Type: cross Abstract: Standard conformal prediction (CP) procedures are typically formulated in terms of p-values, but reliance on p-values alone limits flexibility, for example, when combining dependent evidence across models or data splits.
The paper investigates how watermarking affects recursive discrete distribution estimation when synthetic samples are mixed with real data. It establishes minimax lower bounds showing that, as the proportion of real samples approaches zero, adding watermarks cannot improve performance unless the false‑negative detection rate also vanishes. The authors further demonstrate that simple deterministic estimators achieve worst‑case losses close to these bounds and introduce a masking technique that reduces the remaining performance gap to a Jensen gap, suggesting potential for tighter bounds.
We propose the data augmented bootstrap (DAB), a framework for constructing confidence intervals from approximately invariant transformations of the data. As special cases, DAB recovers popular methods that rely on exact group symmetries, such as conformal prediction, wild bootstrap for Maximum Mean Discrepancy U-statistics and the recently proposed SymmPI.
arXiv:2508.10336v3 Announce Type: replace-cross Abstract: In a supervised online setting, quantifying uncertainty has been proposed in the seminal work of Gibbs and Cand\`es (2021). For any given poi...