从多个群体中选出最优特征，让模型在不同人群中都表现稳定。
Distributionally Robust Feature Selection
- 用噪声机制将特征选择转化为连续优化问题，无需反向传播训练过程。
- 通过优化贝叶斯最优预测器的方差，提升跨群体的平均性能。
- 适用于高成本特征采集场景，如问卷调查或传感器部署。
我们研究在有限特征可观测的情况下，如何选择特征，使基于这些特征训练的模型能在多个子群体中同时表现良好。该问题在特征采集成本较高（如增加问卷问题或安装物理传感器）的场景中具有实际意义，需确保所选特征能为不同人群构建高质量的下游模型。本文提出一种方法，将传统变量选择问题通过噪声机制进行连续松弛，避免对模型训练过程进行反向传播。通过优化贝叶斯最优预测器的方差，构建了一个与模型无关的框架，以平衡不同群体间的整体预测性能。我们在合成数据集和真实世界数据上验证了该方法的有效性。
原文摘要 · Abstract (English)
We study the problem of selecting limited features to observe such that models trained on them can perform well simultaneously across multiple subpopulations. This problem has applications in settings where collecting each feature is costly, e.g. requiring adding survey questions or physical sensors, and we must be able to use the selected features to create high-quality downstream models for different populations. Our method frames the problem as a continuous relaxation of traditional variable selection using a noising mechanism, without requiring backpropagation through model training processes. By optimizing over the variance of a Bayes-optimal predictor, we develop a model-agnostic framework that balances overall performance of downstream prediction across populations. We validate our approach through experiments on both synthetic datasets and real-world data.
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