标题： 通过置信预测扩展预测驱动的推断 显示英文标题

标题： Extending Prediction-Powered Inference through Conformal Prediction

摘要： 预测驱动的推断是一种近期的方法，用于安全地使用黑盒机器学习模型来填补缺失数据，加强统计参数的推断。 然而，许多应用除了有效的推断外，还需要其他强属性，例如隐私性、鲁棒性或在连续分布变化下的有效性；获得具有这些保证的预测驱动方法通常是一个艰巨的过程，必须逐个案例进行。 在本文中，我们通过将预测驱动推断与符合预测相结合来解决这个问题：通过使用校准的符合集合预测器进行填补，我们在自然方式下实现了有效性并获得了额外的保证。 我们针对均值、Z-估计和M-估计，以及e值和基于e值的程序实例化了我们的过程。 此外，在e值的情况下，我们的方法是第一个离线运行的一般预测驱动过程。 我们通过在隐私数据和时间序列数据上应用我们的方法来展示这些优势。 在这两个任务中，标准的预测驱动框架中都是非平凡的，但在我们的方法下变得自然。 显示英文摘要

摘要： Prediction-powered inference is a recent methodology for the safe use of black-box ML models to impute missing data, strengthening inference of statistical parameters. However, many applications require strong properties besides valid inference, such as privacy, robustness or validity under continuous distribution shifts; deriving prediction-powered methods with such guarantees is generally an arduous process, and has to be done case by case. In this paper, we resolve this issue by connecting prediction-powered inference with conformal prediction: by performing imputation through a calibrated conformal set-predictor, we attain validity while achieving additional guarantees in a natural manner. We instantiate our procedure for the inference of means, Z- and M-estimation, as well as e-values and e-value-based procedures. Furthermore, in the case of e-values, ours is the first general prediction-powered procedure that operates off-line. We demonstrate these advantages by applying our method on private and time-series data. Both tasks are nontrivial within the standard prediction-powered framework but become natural under our method.