Prediction-Powered Active Testing
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. To address this, we propose \textbf{Prediction--Powered Active Testing (PPAT)}, a novel label--efficient risk estimation framework that combines the unbiased LURE estimator \citep{farquhar2021statistical} with a prediction--powered control variate. Rather than using proxy predictions as biased pseudo--labels, PPAT uses them to residualise the loss, preserving...
| Authors | Kianoosh Ashouritaklimi, Valentin Kilian, Daolang Huang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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