Accelerating Multi-Objective Bayesian Optimisation via Predictive-Gradient Catalysts
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Computer Science > Machine Learning
Title:Accelerating Multi-Objective Bayesian Optimisation via Predictive-Gradient Catalysts
Abstract:This paper presents a general acceleration mechanism for multi-objective Bayesian optimisation (MOBO) that leverages Gaussian process predictive gradients as auxiliary signals. Rather than replacing existing Pareto-compliant acquisition functions, the proposed approach augments them with local stationarity information derived from surrogate-derived gradients, enabling faster convergence toward the global Pareto set under limited evaluation budgets. Two catalyst instantiations are investigated: an adaptive Multiple-Gradient Descent Algorithm-Based Catalyst (MGDA) and a predefined-weight variant that enables focused exploration when budgets are tight. Experiments on the DTLZ benchmark suite (using 2 objectives and 10 decision variables) show that predictive gradient catalysis can deliver significant acceleration compared to other acquisition functions (EHVI, AugTch, tMPoI, SAF) when surrogates are accurate, particularly for stationary problems.
| Comments: | Parallel Problem Solving From Nature (PPSN), 2026 |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.06984 [cs.LG] |
| (or arXiv:2606.06984v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.06984
arXiv-issued DOI via DataCite (pending registration)
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