arXiv:2610.35821v1 Announce Type: cross
Abstract: Statistical procedures often control expected loss at a prescribed level while leaving part of their risk budget unused. Building on the e-value repr...
By Tian Bai, Aaditya Ramdas
arXiv:2609.38112v1 Announce Type: cross
Abstract: Many applications of black-box predictive models require controlling task-relevant error rates, such as missed lesion pixels in segmentation or misse...
By Bruno Marcondes e Resende, Helton Graziadei, Thiago Rodrigo Ramos, Rafael Izbicki
arXiv:2606. 29054v1 Announce Type: new Abstract: Large language models (LLMs) deployed for structured generation (NER, JSON extraction, QA, and classification) lack formal reliability guarantees, and standard heuristic abstention policies miss user-specified risk targets by 7.
By Varun Kotte
arXiv:2610.22930v1 Announce Type: cross
Abstract: Artificial-intelligence (AI) policies are increasingly used to make consequential decisions in medicine, finance, education, and public services, yet...
By Junyu Cao, Ruijiang Gao, Maytal Saar-Tsechansky, Mingzhang Yin
arXiv:2607. 02206v1 Announce Type: cross Abstract: Predictions are increasingly used to guide high-stakes decisions, from treatment selection to policy making.
By Yurui Zheng, Ying Jin
arXiv:2604. 11305v3 Announce Type: replace Abstract: Conformal selection (CS) uses calibration data to identify test inputs whose unobserved outcomes are likely to satisfy a pre-specified minimal quality requirement, while controlling the false discovery rate (FDR).
By Meiyi Zhu, Osvaldo Simeone