非短视路径策略梯度的主动特征获取
Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients
AI总结：
本文提出非短视路径策略梯度方法，通过连续放松获取过程并引入直通滚动生成方案，实现非短视特征获取的端到端优化，实验证明其在合成和真实数据集上优于现有方法。
摘要：
主动特征获取（AFA）考虑预测问题中特征获取成本高且学习器需自适应决定每个实例的特征值获取及何时停止预测的问题。AFA可建模为部分可观测马尔可夫决策过程（POMDP），自然允许序贯决策视角。本文提出非短视路径策略梯度（NM-PPG），一种基于此建模的新AFA方法。我们引入连续放松的获取过程，通过完整获取轨迹生成路径梯度，避免标准分数函数策略梯度的高方差，同时允许非短视获取策略的端到端优化。为进一步使训练与部署对齐，我们进一步开发了直通滚动生成方案，在正向传递中遵循硬特征获取，在反向传递中通过相应的软放松进行反向传播。我们通过熵正则化和分阶段温度锐化稳定优化。在合成和真实数据集上的实验表明，NM-PPG在性能上优于现有最先进的AFA基线。
英文摘要：
Active feature acquisition (AFA) considers prediction problems in which features are costly to obtain and the learner adaptively decides which feature values to acquire for each instance and when to stop and predict. In this paper, we introduce a continuous relaxation of the acquisition process that enables non-myopic pathwise policy gradients (NM-PPG) through the full acquisition trajectory, avoiding the high variance of standard score-function policy gradients while allowing end-to-end optimization of the acquisition policy. To better align training with deployment, we develop a straight-through rollout that follows discrete feature acquisitions in the forward pass while backpropagating through the corresponding soft relaxation. We derive an AFA-specific average-case upper bound on the variance of this gradient estimator, which characterizes instability and motivates staged temperature sharpening. Experiments on both synthetic and real-world datasets demonstrate that NM-PPG yields superior performance relative to state-of-the-art AFA baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
