On the Brittleness of Maximum Likelihood Estimation for Gaussian Process Hyperparameter Optimization
Machine learning (ML) has become an indispensable part of modern engineering design workflows. A crucial step in training an ML model is the selection of the loss function which can be systematically formulated via various techniques such as maximum likelihood estimation (MLE) and cross-validation . While MLE is one of the most popular, effective, and intuitive mechanisms for training ML models, it is brittle: if the assumptions underpinning it are not met, the trained ML model may generalize poorly. This brittleness affects even Gaussian processes (GPs) which are widely used in engineering design and are often (incorrectly) presumed to be very robust to overfitting. In this paper, we fun...
| Authors | Tyler R. Johnson, Kian Ben-Jacob, Christopher P. Muller et al. |
|---|---|
| Primary category | stat.ML |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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