HyperBO regret beyond finite-dimensional GP assumptions
HyperBO regret beyond finite-dimensional GP assumptions. Current research status is unassessed. The dated source poses this question; inclusion does not certify that it remains open today.
Editorial MSC2020 mapping of HyperBO regret beyond finite-dimensional GP assumptions. https://jmlr.org/papers/volume25/23-0269/23-0269.pdf#page=49 Not an author-supplied code.
Problem statement
Establish regret guarantees for pre-trained Gaussian processes with expressive means and kernels on continuous domains, relaxing the finite-dimensional restriction of Assumption 3.
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Background
Editorial question summary. Consult the linked source for the complete model, assumptions and discussion.
Progress & source assessment
Current research status is unassessed. The dated source poses this question; inclusion does not certify that it remains open today.
- [1]Zi Wang; George E. Dahl; Kevin Swersky; Chansoo Lee; Zachary Nado; Justin Gilmer; Jasper Snoek; Zoubin Ghahramani. JMLR — Pre-trained Gaussian Processes for Bayesian Optimization (2024).; HyperBO regret beyond finite-dimensional GP assumptions linkCC BY 4.0; attributed editorial adaptation of the named section authors.
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Discussion
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