高维水平集估计新算法，采样效率更高
High-dimensional Level Set Estimation with Trust Regions and Double Acquisition Functions
- 双采集函数协同工作，全局与局部并行优化
- 在多个真实与合成数据集上样本效率优于现有方法
- 适合高维空间中需高效获取信息的场景
水平集估计（LSE）旨在判断未知函数值是否超过指定阈值，是众多实际应用中的基础问题。在初始数据有限的主动学习设置下，我们目标是迭代获取有信息量的点，构建准确的分类器。在高维空间中，搜索空间随维度呈指数增长，使该任务极具挑战。本文提出TRLSE算法，通过双采集函数在全局与局部层面识别并精炼接近阈值边界的区域。我们提供了TRLSE精度的理论分析，并通过在多个合成与真实世界LSE问题上的广泛评估，验证其在样本效率方面优于现有方法。
原文摘要 · Abstract (English)
Level set estimation (LSE) classifies whether an unknown function's value exceeds a specified threshold for given inputs, a fundamental problem in many real-world applications. In active learning settings with limited initial data, we aim to iteratively acquire informative points to construct an accurate classifier for this task. In high-dimensional spaces, this becomes challenging where the search volume grows exponentially with increasing dimensionality. We propose TRLSE, an algorithm for high-dimensional LSE, which identifies and refines regions near the threshold boundary with dual acquisition functions operating at both global and local levels. We provide a theoretical analysis of TRLSE's accuracy and show its superior sample efficiency against existing methods through extensive evaluations on multiple synthetic and real-world LSE problems.
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