High-dimensional Level Set Estimation with Trust Regions and Double Acquisition Functions
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912901925699584 |
|---|---|
| author | Ngo, Giang Trong, Dat Phan Nguyen, Dang Gupta, Sunil |
| author_facet | Ngo, Giang Trong, Dat Phan Nguyen, Dang Gupta, Sunil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_12391 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | High-dimensional Level Set Estimation with Trust Regions and Double Acquisition Functions Ngo, Giang Trong, Dat Phan Nguyen, Dang Gupta, Sunil Machine Learning 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. |
| title | High-dimensional Level Set Estimation with Trust Regions and Double Acquisition Functions |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.12391 |