arXiv Machine Learning By Meshi Bashari, Yonghoon Lee, Roy Maor Lotan, Edgar Dobriban, Yaniv Romano

General Synthetic-Powered Inference

Read the original on arXiv Machine Learning →

arXiv:2509. 20345v3 Announce Type: replace-cross Abstract: The rapid proliferation of high-quality synthetic data -- generated by advanced AI models or collected as auxiliary data from related tasks -- presents both opportunities and challenges for statistical inference.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jul 10

Prediction-Powered Active Testing

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 Machine Learning
Aug 11

Demystifying Prediction Powered Inference

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