Active Sampling: A Machine-Learning-Assisted Framework for Finite Population Inference with Optimal Subsamples
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. W...
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| Vydáno v: | Technometrics Ročník 67; číslo 1; s. 46 - 57 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Alexandria
Taylor & Francis
02.01.2025
American Society for Quality |
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| ISSN: | 0040-1706, 1537-2723, 1537-2723 |
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| 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. |
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| AbstractList | 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. |
| Author | Flannagan, Carol Yang, Xiaomi Bärgman, Jonas Imberg, Henrik |
| Author_xml | – sequence: 1 givenname: Henrik orcidid: 0000-0001-9447-663X surname: Imberg fullname: Imberg, Henrik organization: Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg – sequence: 2 givenname: Xiaomi orcidid: 0000-0003-1641-9634 surname: Yang fullname: Yang, Xiaomi organization: Division of Vehicle Safety, Chalmers University of Technology – sequence: 3 givenname: Carol orcidid: 0000-0001-8484-4187 surname: Flannagan fullname: Flannagan, Carol organization: University of Michigan Transportation Research Institute – sequence: 4 givenname: Jonas orcidid: 0000-0002-3578-2546 surname: Bärgman fullname: Bärgman, Jonas organization: Division of Vehicle Safety, Chalmers University of Technology |
| BackLink | https://gup.ub.gu.se/publication/341566$$DView record from Swedish Publication Index (Göteborgs universitet) https://research.chalmers.se/publication/543038$$DView record from Swedish Publication Index (Chalmers tekniska högskola) |
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| SubjectTerms | Active learning Adaptive importance sampling Adaptive sampling Adaptiveimportance sampling Advanced driver assistance systems Bayesian analysis Computer and Information Sciences Computer simulation experiments Computer simulationexperiments Data collection Data- och informationsvetenskap (Datateknik) Estimating techniques Importance sampling Inverse probability weighting Inverseprobability weighting Machine learning Massive data points Optimal design Sampling methods Simulation Software Traffic safety assessment Traffic safetyassessment |
| Title | Active Sampling: A Machine-Learning-Assisted Framework for Finite Population Inference with Optimal Subsamples |
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