arXiv:2601. 20819v2 Announce Type: replace-cross Abstract: Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical research, environmental science, and social science.
By Yilin Song, Dan M. Kluger, Harsh Parikh, Tian Gu
arXiv:2607. 08347v1 Announce Type: cross Abstract: Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled.
By Kianoosh Ashouritaklimi, Valentin Kilian, Daolang Huang, Tom Rainforth, Fran\c{c}ois Caron
arXiv:2505. 20178v2 Announce Type: replace-cross Abstract: Prediction-Powered Inference (PPI) is a popular strategy for combining gold-standard and possibly noisy pseudo-labels to perform statistical estimation.
By Pranav Mani, Peng Xu, Zachary C. Lipton, Michael Oberst
Active testing provides a label--efficient approach to risk estimation by adaptively selecting which test points should be labelled. However, existing estimators fail to exploit the informative predictions of powerful black--box models, even though such predictions are increasingly available in settings where labels remain expensive.
The paper introduces Conditional Poisson Sampling (CPS) as a fixed-size alternative to Poisson Sampling (PS) for variance reduction in statistical inference. CPS can replace PS in multivariate Z-estimation problems, including convex M-estimation, without increasing asymptotic variance and often improving it when predictions are imperfect. The authors demonstrate these benefits on synthetic and real datasets and provide a Python package for CPS implementation.
By Erik Skalnes, Michael Oberst
arXiv:2603.05575v2 Announce Type: replace-cross
Abstract: We study prediction-powered conditional inference in the setting where labeled data are scarce, unlabeled covariates are abundant, and a blac...
By Yang Sui, Jin Zhou, Hua Zhou, Xiaowu Dai