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:2609.27179v2 Announce Type: replace-cross
Abstract: We study the challenging problem of distribution-free sequential changepoint detection for independent observations with unknown and \emph{co...
By Swapnaneel Bhattacharyya, 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:2509. 11208v3 Announce Type: replace-cross Abstract: Transformers used for evidence-grounded binary adjudication (e.
By Leon Chlon, Ahmed Karim, Maggie Chlon, MarcAntonio Awada
arXiv:2511. 01064v3 Announce Type: replace-cross Abstract: Variational inference (VI) approximates a target density $p$ by the best match $q$ in a family of tractable distributions.
By Charles C. Margossian, Isaac E. Rankin, Lawrence K. Saul
arXiv:2606. 20183v1 Announce Type: new Abstract: Recent quantum vision models-quantum vision transformers and quantum convolutional networks-report two striking but unexplained empirical phenomena: (i) ansatze with more, or more uniformly distributed, entanglement generalize better, and (ii) injecting quantum noise can improve test accuracy rather than degrade it.
By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao