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arXiv · cs.AI· Yunni Qu (Department of Computer Science, University of North Carolina at Chapel Hill), Bing Cai Kok (Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, School of Social Sciences, Nanyang Technological University, Singapore), Whitney Ringwald (Department of Psychology, University of Minnesota Twin Cities), Grant King (Department of Psychology, University of Michigan), Aidan Wright (Department of Psychology, University of Michigan), Kathleen Gates (Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill), Junier Oliva (Department of Computer Science, University of North Carolina at Chapel Hill)·· 2 天前

纵向主动特征采集（LAFA）：通过树蒸馏实现低成本时序预测并降低参与者负担

Active Feature Acquisition for Cost-Efficient Temporal Prediction with Reduced Participant Burden

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作者：Yunni Qu (Department of Computer Science, University of North Carolina at Chapel Hill), Bing Cai Kok (Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, School of Social Sciences, Nanyang Technological University, Singapore), Whitney Ringwald (Department of Psychology, University of Minnesota Twin Cities), Grant King (Department of Psychology, University of Michigan), Aidan Wright (Department of Psychology, Kathleen Gates (Department of Psychology and Neuroscience, Junier Oliva (Department of Computer Science

研究任务与主要进展

研究提出一种从基于神经网络（NN）的LAFA网络中学习可解释策略的树蒸馏方法，用于选择各时间点需要采集的变量子集。基于论文摘要，该方法通过模拟和经验EMA数据集验证，在预测每日酒精摄入量时，均能显著减少每次采集的条目数量，同时仅造成较小的准确率损失。

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来源：arXiv · cs.AI · arxiv.org

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