论文标题
主动采样：用于有限种群推断的机器学习辅助框架，最佳子样本
Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples
论文作者
论文摘要
数据子采样已被广泛认为是在分析大量数据集中克服计算和经济瓶颈的工具。我们使用主动学习和自适应重要性抽样为自适应设计的发展做出了贡献。我们提出了一种主动抽样策略，该策略在估计和数据收集之间以最佳的子样本进行迭代，并由机器学习预测对却看不见的数据进行指导。该方法在基于虚拟模拟的高级驾驶员辅助系统的安全性评估中进行了说明。与传统的抽样方法相比，证明了大量的性能改进。
Data subsampling has become widely recognized as a tool to overcome computational and economic bottlenecks in analyzing massive datasets. We contribute to the development of adaptive design for estimation of finite population characteristics, using active learning and adaptive importance sampling. We propose an active sampling strategy that iterates between estimation and data collection with optimal subsamples, guided by machine learning predictions on yet unseen data. The method is illustrated on virtual simulation-based safety assessment of advanced driver assistance systems. Substantial performance improvements are demonstrated compared to traditional sampling methods.
