<ul><li>Bayesian optimization is an effective technique for black-box optimization, but its applicability is limited for large-budget problems due to computational complexity.</li><li>Researchers propose Gradient-based Sample Selection Bayesian Optimization (GSSBO) to improve the computational efficiency of Bayesian optimization.</li><li>GSSBO constructs the Gaussian process (GP) model on a selected set of samples using gradient information.</li><li>The approach reduces the computational cost of GP fitting while maintaining optimization performance similar to baseline methods.</li></ul>

Gradient-based Sample Selection for Faster Bayesian Optimization

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