Fully sequential Gaussian process regression for preference learning
We study continuous global optimization problems in which the objective function cannot be directly evaluated; rather, we observe only which of two selected design points appears to perform better, subject to noise. Such comparisons complicate the use of Gaussian process regression because they yield non-Gaussian posteriors. We consider a fully sequential approximation, based on moment-matching, that yields a fast closed-form update of the metamodel's mean and covariance. We then propose DOVE, a fully sequential sampling policy that combines an information gain criterion with randomized sampling designed to control error accumulation. Under fairly standard regularity conditions, we prove ...
| Authors | Ilya O. Ryzhov |
|---|---|
| Primary category | cs.LG |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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