近日，美国加州大学的Tijana Zrnic及其研究小组取得一项新进展。经过不懈努力，他们提出预测驱动推理框架。相关研究成果已于
预测驱动推理是一个框架，当机器学习系统的预测得到实验数据集的补充时，该框架可用于执行有效的统计推理。该框架能够衍生出简单的算法，用于计算可证明有效的置信区间，如平均值、分位数、线性和逻辑回归系数，而无需对提供预测的机器学习算法进行任何假设。
此外，更准确的预测意味着更小的置信区间。预测驱动推理可以使研究人员利用机器学习得出有效的、更数据高效的结论。研究人员通过蛋白质组学、天文学、基因组学、遥感、人口普查分析和生态学的数据集证明了预测驱动推理的优势。
附：英文原文
Title: Prediction-powered inference
Author: Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I. Jordan, Tijana Zrnic
Issue&Volume: 2023-11-10
Abstract: Prediction-powered inference is a framework for performing valid statistical inference when an experimental dataset is supplemented with predictions from a machine-learning system. The framework yields simple algorithms for computing provably valid confidence intervals for quantities such as means, quantiles, and linear and logistic regression coefficients without making any assumptions about the machine-learning algorithm that supplies the predictions. Furthermore, more accurate predictions translate to smaller confidence intervals. Prediction-powered inference could enable researchers to draw valid and more data-efficient conclusions using machine learning. The benefits of prediction-powered inference were demonstrated with datasets from proteomics, astronomy, genomics, remote sensing, census analysis, and ecology.
DOI: 10.1126/science.adi6000
Source: https://www.science.org/doi/10.1126/science.adi6000