Prediction Aided by Surrogate Training
代理训练辅助的预测AI 解读 ↓
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代理训练辅助的预测
- 研究问题
- 当只有少量样本具有真实响应、全部样本均有辅助协变量时，如何仅在训练阶段利用辅助信息，改善最终只依赖标准协变量的预测？
- 方法与思路
- 作者提出 PAST：先用标注数据及两类协变量构造响应估计，再以全样本伪响应训练仅使用标准协变量的预测器。
- 材料陈述的贡献
- 摘要称建立由完整响应数据的基准风险与伪响应误差构成的预测误差上界，刻画接近基准及表现较差的情形，并展示多种应用。
- 阅读关注点
- 应核对响应估计误差如何传递至最终风险、经验风险最小化所需条件，以及未来协变量作为训练辅助信息时的样本划分与可用性。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未列出具体风险界条件及实证数值，辅助信息并不保证总能改善预测。
原文摘要
We study a class of prediction problems in which relatively few observations have associated responses, but all observations include both standard covariates as well as additional "helper" covariates. While the end goal is to make high-quality predictions using only the standard covariates, helper covariates can be exploited during training to improve prediction. Helper covariates arise in many applications, including forecasting in time series; incorporation of biased or mis-calibrated predictions from foundation models; and sharing information in transfer learning. We propose "prediction aided by surrogate training" ($\texttt{PAST}$), a class of methods that exploit labeled data to construct a response estimator based on both the standard and helper covariates; and then use the full dataset with pseudo-responses to train a predictor based only on standard covariates. We establish guarantees on the prediction error of this procedure, with the response estimator allowed to be constructed in an arbitrary way, and the final predictor fit by empirical risk minimization over an arbitrary function class. These upper bounds involve the risk associated with the oracle data set (all responses available), plus an overhead that measures the accuracy of the pseudo-responses. This theory characterizes both regimes in which $\texttt{PAST}$ accuracy is comparable to the oracle accuracy, as well as more challenging regimes where it behaves poorly. We demonstrate its empirical performance across a range of applications, including forecasting of societal ills over time with future covariates as helpers; prediction of cardiovascular risk after heart attacks with prescription data as helpers; and diagnosing pneumonia from chest X-rays using machine-generated predictions as helpers.