为个性化预测设计可解释的稀疏线性分类器，提升高风险场景下的模型可信度。
Personalized Prediction By Learning Halfspace Reference Classes Under Well-Behaved Distribution
- 基于半空间定义子群体，为每个查询点学习可解释的稀疏线性分类器
- 首次证明在同质半空间子集上，个性化预测误差上界为 $O(\mathrm{opt}^{1/4})$
- 适用于医疗等需可解释性的高风险决策场景
在机器学习应用中，预测模型通常针对整个数据分布进行训练。然而，现实数据常需复杂模型才能达到良好性能，牺牲了可解释性。因此，在医疗等高风险场景中部署机器学习模型时，亟需兼顾准确性和可解释性的方法。本文提出一种个性化预测方案：为每个查询点学习一个易于理解的预测器。具体而言，目标是在包含该查询点的某个子群体上，构建具有竞争力性能的“稀疏线性”分类器。本文研究在标签无关设置下，以“半空间”表示的子群体的PAC可学习性。首先给出一种针对特定分布的参考类学习算法；结合该算法与稀疏线性表示的列表学习器，首次证明了在同质半空间子集上，个性化预测使用稀疏线性分类器的误差上界为 $O(\mathrm{opt}^{1/4})$。我们在多个标准基准数据集上评估了所提算法。
原文摘要 · Abstract (English)
In machine learning applications, predictive models are trained to serve future queries across the entire data distribution. Real-world data often demands excessively complex models to achieve competitive performance, however, sacrificing interpretability. Hence, the growing deployment of machine learning models in high-stakes applications, such as healthcare, motivates the search for methods for accurate and explainable predictions. This work proposes a Personalized Prediction scheme, where an easy-to-interpret predictor is learned per query. In particular, we wish to produce a "sparse linear" classifier with competitive performance specifically on some sub-population that includes the query point. The goal of this work is to study the PAC-learnability of this prediction model for sub-populations represented by "halfspaces" in a label-agnostic setting. We first give a distribution-specific PAC-learning algorithm for learning reference classes for personalized prediction. By leveraging both the reference-class learning algorithm and a list learner of sparse linear representations, we prove the first upper bound, $O(\mathrm{opt}^{1/4} )$, for personalized prediction with sparse linear classifiers and homogeneous halfspace subsets. We also evaluate our algorithms on a variety of standard benchmark data sets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。