ALAS: Additive Learnable Alpha-Stable Kernels for Flexible Bayesian Optimization
For practitioners of Bayesian optimization, ALAS provides a robust kernel that automatically adapts to unknown objective structures, reducing the need for manual kernel selection.
ALAS introduces a flexible Gaussian Process kernel family based on symmetric α-stable spectral components that adapts smoothness by learning the stability parameter α, achieving robust performance across diverse Bayesian optimization benchmarks and real-world surrogates.
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 $α$-stable spectral components. By learning the stability parameter $α$, ALAS adapts its effective smoothness from data, capturing both smooth trends and sharp irregularities. We present two parameterizations: ALAS, a single stationary component with joint spectral modulation, and ALAS-Sep, a separable variant that learns dimension-wise tail behavior to improve robustness on approximately decomposable objectives. Experiments on standard benchmarks and real-world surrogates demonstrate strong and robust performance across diverse settings.