Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866916310995173376 |
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| author | Imberg, Henrik Yang, Xiaomi Flannagan, Carol Bärgman, Jonas |
| author_facet | Imberg, Henrik Yang, Xiaomi Flannagan, Carol Bärgman, Jonas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_10024 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples Imberg, Henrik Yang, Xiaomi Flannagan, Carol Bärgman, Jonas Methodology Applications Computation 62D99, 62K05, 62L05, 62P30 G.3 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. |
| title | Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples |
| topic | Methodology Applications Computation 62D99, 62K05, 62L05, 62P30 G.3 |
| url | https://arxiv.org/abs/2212.10024 |