Abstract
The remarkable success of machine learning (ML) in predictive tasks has led scientists to incorporate ML predictions as a core component of the scientific discovery pipeline. This was exemplified by the landmark achievement of AlphaFold (Jumper et al. (2021)). In this paper, we study how ML predictions can be safely used in statistical analysis of data towards scientific discovery. In particular, we follow the framework introduced by Angelopoulos et al. (2023). In this framework, we assume access to a small set of n gold-standard labeled samples, a much larger set of N unlabeled samples, and a ML model that can be used to impute the labels of the unlabeled data points. We introduce two new learning-augmented estimators, (1) Prediction-Augmented Residual Tree (PART) and (2) Prediction-Augmented Quadrature (PAQ). Both estimators have significant advantages over existing estimators like PPI and PPI++ introduced by Angelopoulos et al. (2023) and Angelopoulos et al. (2024), respectively.
