arXiv cs.LG
· Papers
ALAS: Additive Learnable Alpha-Stable Kernels for Flexible Bayesian Optimization
arXiv:2607.18282v1 Announce Type: new Abstract: Bayesian Optimization is widely used for expensive black-box optimization, yet its success often depends on choosing a kernel that matches the objective's unknown structure. In this work, we propose ALAS, a flexible Gaussian Process kernel family built from symmetric $al