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 $K \geq 0$.
By Rohan Hore, Aaditya Ramdas
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.
By Shalev Shaer, Yarin Bar, Drew Prinster, Yaniv Romano
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...
By Swapnaneel Bhattacharyya, Aaditya Ramdas
arXiv:2505. 04608v5 Announce Type: replace-cross Abstract: Responsibly deploying artificial intelligence (AI) / machine learning (ML) systems in high-stakes settings arguably requires not only proof of system reliability, but also continual, post-deployment monitoring to quickly detect and address any unsafe behavior.
By Drew Prinster, Xing Han, Anqi Liu, Suchi Saria
The paper introduces a sequential change‑point detection method for time‑ordered data where neither the pre‑ nor post‑change distributions have closed forms. It trains a conditional diffusion model on pre‑change data, uses its probability flow ODE to map observations to a Gaussian latent space, and then applies the Maximum Mean Discrepancy as a test statistic. The authors derive closed‑form components under the Gaussian null, establish the statistic’s asymptotic distribution as a degenerate U‑statistic, and implement an online Shiryaev–Roberts procedure with exact threshold calibration to detect arbitrary distributional shifts without parametric assumptions.
By Artem Kraevskiy, Artem Prokhorov
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.
By Jungbin Jun, Ilsang Ohn