Proposes a vector-valued RKHS framework for Bayesian optimization with structured measurements, deriving concentration bounds and UCB-based regret guarantees that recover sublinear rates.
On controller tuning with time-varying Bayesian optimization
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Bayesian Optimization with Structured Measurements: A Vector-Valued RKHS Framework
Proposes a vector-valued RKHS framework for Bayesian optimization with structured measurements, deriving concentration bounds and UCB-based regret guarantees that recover sublinear rates.