A Geometric Approach to Optimal Experimental Design
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909850434273280 |
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| author | Kerrigan, Gavin Naesseth, Christian A. Rainforth, Tom |
| author_facet | Kerrigan, Gavin Naesseth, Christian A. Rainforth, Tom |
| contents | We introduce a novel geometric framework for optimal experimental design (OED). Traditional OED approaches, such as those based on mutual information, rely explicitly on probability densities, leading to restrictive invariance properties. To address these limitations, we propose the mutual transport dependence (MTD), a measure of statistical dependence grounded in optimal transport theory which provides a geometric objective for optimizing designs. Unlike conventional approaches, the MTD can be tailored to specific downstream estimation problems by choosing appropriate geometries on the underlying spaces. We demonstrate that our framework produces high-quality designs while offering a flexible alternative to standard information-theoretic techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14848 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Geometric Approach to Optimal Experimental Design Kerrigan, Gavin Naesseth, Christian A. Rainforth, Tom Machine Learning We introduce a novel geometric framework for optimal experimental design (OED). Traditional OED approaches, such as those based on mutual information, rely explicitly on probability densities, leading to restrictive invariance properties. To address these limitations, we propose the mutual transport dependence (MTD), a measure of statistical dependence grounded in optimal transport theory which provides a geometric objective for optimizing designs. Unlike conventional approaches, the MTD can be tailored to specific downstream estimation problems by choosing appropriate geometries on the underlying spaces. We demonstrate that our framework produces high-quality designs while offering a flexible alternative to standard information-theoretic techniques. |
| title | A Geometric Approach to Optimal Experimental Design |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.14848 |