Experimental Design Using Interlacing Polynomials
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866912073375547392 |
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| author | Lau, Lap Chi Wang, Robert Zhou, Hong |
| author_facet | Lau, Lap Chi Wang, Robert Zhou, Hong |
| contents | We present a unified deterministic approach for experimental design problems using the method of interlacing polynomials. Our framework recovers the best-known approximation guarantees for the well-studied D/A/E-design problems with simple analysis. Furthermore, we obtain improved non-trivial approximation guarantee for E-design in the challenging small budget regime. Additionally, our approach provides an optimal approximation guarantee for a generalized ratio objective that generalizes both D-design and A-design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11390 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Experimental Design Using Interlacing Polynomials Lau, Lap Chi Wang, Robert Zhou, Hong Data Structures and Algorithms Machine Learning Computation We present a unified deterministic approach for experimental design problems using the method of interlacing polynomials. Our framework recovers the best-known approximation guarantees for the well-studied D/A/E-design problems with simple analysis. Furthermore, we obtain improved non-trivial approximation guarantee for E-design in the challenging small budget regime. Additionally, our approach provides an optimal approximation guarantee for a generalized ratio objective that generalizes both D-design and A-design. |
| title | Experimental Design Using Interlacing Polynomials |
| topic | Data Structures and Algorithms Machine Learning Computation |
| url | https://arxiv.org/abs/2410.11390 |