KSOS-BO improves acquisition function optimization in Bayesian optimization by casting it as a kernel sum of squares semidefinite program, outperforming Sobol, DE, and CMA-ES baselines on 10/15 benchmarks with 81% average regret reduction.
Nonlinear opti- mal control via occupation measures and LMI-relaxations.SIAM Journal on Control and Optimization, 47(4):1643–1666
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KSOS-BO: Improving Sampling in Bayesian Optimization via Kernel Sum of Squares
KSOS-BO improves acquisition function optimization in Bayesian optimization by casting it as a kernel sum of squares semidefinite program, outperforming Sobol, DE, and CMA-ES baselines on 10/15 benchmarks with 81% average regret reduction.