Conference item
A geometric approach to optimal experimental design
- Abstract:
-
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.
- Publication status:
- Accepted
- Peer review status:
- Peer reviewed
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- Publication website:
- https://openreview.net/forum?id=u0aepMHQ5p
Authors
- Funder identifier:
- https://ror.org/001aqnf71
- Grant:
- EP/Y037200/1
- Publisher:
- Society for Artificial Intelligence and Statistics
- Acceptance date:
- 2026-04-01
- Event title:
- 29th International Conference on Artificial Intelligence and Statistics (AISTATS 2026)
- Event location:
- Tangier, Morocco
- Event website:
- https://virtual.aistats.org/Conferences/2026
- Event start date:
- 2026-05-02
- Event end date:
- 2026-05-05
- Language:
-
English
- Pubs id:
-
2406613
- Local pid:
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pubs:2406613
- Deposit date:
-
2026-04-15
- ARK identifier:
Terms of use
- Notes:
-
This conference paper has been accepted for presentation at the 29th International Conference on Artificial Intelligence and Statistics (AISTATS 2026), May 2nd – May 5th, 2026, Tangier, Morocco.
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