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
arXiv Machine Learning
Oct 7

Performative Prediction with Selective Labels

arXiv:2610.08272v1 Announce Type: new Abstract: Many social applications of machine learning exhibit performative effects: population behavior changes in response to deployed models. Performative pre...

By Giovani Valdrighi, Isabel Valera, Marcos Medeiros Raimundo
arXiv AI
Jun 6

Correcting Prompt Dependence in LLM Benchmarks: A Bayesian Hierarchical Model with Embedding-Space Clustering

arXiv:2510. 05709v2 Announce Type: replace-cross Abstract: LLM benchmarking metrics often misstate performance and uncertainty as they rely on two assumptions that frequently do not hold in practice: (i) a sufficient number of evaluations are available for classical inference, and (ii) test prompts are independent.

By Mary Llewellyn, Isobel Thornton, James Bishop, Annie Gray