Tag: hyper

Generative Bayesian Hyperparameter Tuning arXiv:2512.20051v1 Announce Type: new Abstract: noindent Hyper-parameter selection is a central practical problem in modern machine learning, governing regularization strength, model capacity, and robustness choices. Cross-validation is often computationally prohibitive at scale, while fully Bayesian hyper-parameter learning can be difficult due to the cost of posterior sampling. We develop a generative…