Seminar: Henrik Imberg, Univeristy of Gothenburg
Seminar
Date: Wednesday 18 December 2024
Time: 13.00 – 14.00
Location: Campus Albano, lecture room 29, house 4, level 2
Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples
Abstract
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. The presentation is based on our recent Technometrics paper, published in 2024, available at: https://doi.org/10.1080/00401706.2024.2374554
Last updated: December 13, 2024
Source: Department of Statistics