AB-SID-iVAR enables Gaussian process active learning for self-induced Boltzmann distributions by closed-form approximation of the target, with high-probability error vanishing guarantees and empirical gains on PES and drug discovery tasks.
and Zhe, Shandian , month = mar, year =
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A multi-source extension of constrained Max-value Entropy Search for Bayesian optimization incorporates auxiliary data sources to improve early exploration and performance under constraints even with weak correlations.
Vanishing gradients from GP initialization explain HDBO failures; MLE of length scales suffices for SOTA performance, enabling the MSR variant on real-world tasks.
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Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights
AB-SID-iVAR enables Gaussian process active learning for self-induced Boltzmann distributions by closed-form approximation of the target, with high-probability error vanishing guarantees and empirical gains on PES and drug discovery tasks.
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Constrained Bayesian Optimisation with Multiple Information Sources
A multi-source extension of constrained Max-value Entropy Search for Bayesian optimization incorporates auxiliary data sources to improve early exploration and performance under constraints even with weak correlations.
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Understanding High-Dimensional Bayesian Optimization
Vanishing gradients from GP initialization explain HDBO failures; MLE of length scales suffices for SOTA performance, enabling the MSR variant on real-world tasks.