A two-stage GP approach with virtual samples and posterior adjustment factors incorporates per-variable monotonic hunches into Bayesian optimization while preserving convergence guarantees, showing faster convergence on simulations and real polymer/scaffolding tasks.
Initializing bayesian hyper- parameter optimization via meta-learning
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Accelerating Experimental Design by Incorporating Experimenter Hunches
A two-stage GP approach with virtual samples and posterior adjustment factors incorporates per-variable monotonic hunches into Bayesian optimization while preserving convergence guarantees, showing faster convergence on simulations and real polymer/scaffolding tasks.