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.
arXiv preprint arXiv:2502.09198 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
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Proposes SPMO framework with ESPI acquisition function to find one high-quality single solution in many-objective BO under limited budgets instead of approximating the entire Pareto front.
citing papers explorer
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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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Do We Really Need to Approach the Entire Pareto Front in Many-Objective Bayesian Optimisation?
Proposes SPMO framework with ESPI acquisition function to find one high-quality single solution in many-objective BO under limited budgets instead of approximating the entire Pareto front.