Kernel-based guarantees for nonlinear parametric models in Bayesian optimization
Modern Bayesian optimization and adaptive sampling methods increasingly rely on nonlinear parametric models, yet theoretical guarantees for such models under adaptive data collection remain limited. Existing analyses largely focus on Gaussian processes, kernel machines, linear models, or linearized neural approximations, leaving a gap between theory and the nonlinear models used in practice. We develop a kernel based framework for analyzing regularized nonlinear parametric models trained on adaptively collected data. Our approach uses kernels over the parameter space to induce reproducing kernel Hilbert space structures over the corresponding model class, yielding confidence bounds for mo...
| Authors | Rafael Oliveira |
|---|---|
| Primary category | stat.ML |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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