LOCO-AdaMP: Built-in LOCO Inference for Adaptive Minipatch Ensembles with Enhanced Prediction
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LOCO-AdaMP：兼顾预测与内置变量重要性推断的自适应小块集成
- 研究问题
- 能否通过自适应特征抽样改善小块集成在高维稀疏情形下的预测，同时保持无需数据分割的变量重要性推断？
- 方法与思路
- 作者以 LOCO 重要性引导特征抽样，结合留二扰动界与观测子抽样带来的分数稳定性，分析自适应过程的依赖。
- 材料陈述的贡献
- 摘要称该框架保留渐近有效的重要性推断，并在模拟与真实数据中改善预测、推断功效和稳定性，且适配不同基学习器。
- 阅读关注点
- 应核对自适应更新规则、渐近有效性的条件、稳定性界和计算成本，以及经验比较是否统一调参和评价标准。
- 阅读边界
- 这是 arXiv v1 预印本，同行评审或期刊接收未获核实；摘要未列出理论条件，变量重要性也不能直接解释为因果效应。
原文摘要
As black-box machine learning models become increasingly common, extracting interpretations with uncertainty quantification has become a critical challenge. One popular type of interpretation is leave-one-covariate-out (LOCO) feature importance, while prior LOCO inference methods often require data-splitting or model-refitting. A recent ensemble framework, LOCO-MP, addresses these challenges using minipatches that subsample both observations and features, but massive feature subsampling can hurt prediction in high-dimensional sparse settings. Motivated by this limitation, we consider minipatch ensembles with adaptive feature sampling guided by LOCO importance, and propose LOCO-AdaMP, which enables free LOCO inference for the resulting adaptive minipatch ensemble. We show that LOCO-AdaMP yields substantially improved predictive models while retaining asymptotically valid feature importance inference without data-splitting, despite the complex dependence between the adaptive sampling distribution and the LOCO importance statistics. Our analysis relies on a careful leave-two-out perturbation bound for the iteratively updated sampling probabilities together with the stability of LOCO scores induced by observation subsampling. Empirical results on synthetic and real datasets demonstrate advantages of LOCO-AdaMP over existing methods in predictive performance, inferential power, and stability. Overall, LOCO-AdaMP provides a flexible ensemble framework (agnostic to base models) that delivers both strong predictive performance and asymptotically valid, powerful feature importance inference for regression.