<ul data-eligibleForWebStory="false"><li>The availability of machine learning systems has led to the use of synthetic labels in statistical inference applications.</li><li>The Prediction Powered Inference (PPI) framework aims to combine pseudo-labelled data with a small sample of real high-quality labels for efficient evaluation.</li><li>When labelled data is scarce, the PPI++ method may perform poorly compared to traditional inference methods like ordinary least squares regression.</li><li>The study relates PPI++ to regression techniques and introduces new PPI-based approaches that utilize robust regressors for improved estimation in scenarios with few labels.</li></ul>

Regression for the Mean: Auto-Evaluation and Inference with Few Labels through Post-hoc Regression

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