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 $K \geq 0$.
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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.