No-Regret Bayesian Optimization with Finite-Library Input-Warped Kernels
Gaussian-process Bayesian optimization (GP-BO) excels at black-box optimization of costly functions, e.g., hyperparameter optimization (HPO) and multi-agent system (MAS) design. Convergence-rate guarantees exist for select methods, notably GP upper confidence bound (GP-UCB), but require a fixed kernel. Critically, the kernel encodes how input proximity affects objective value similarity. When raw coordinates poorly match this geometry - as with log-scaled hyperparameters or localized peaks - input warping can greatly improve sample efficiency, yet known GP-UCB proofs require a fixed kernel. We propose Finite-Library Input-Warped Bayesian Optimization (FLIWBO), which selects warps from a f...
| Authors | Edvin Ketabati Augustinsson, Robert A. Bridges |
|---|---|
| Primary category | cs.LG |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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