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This research introduces a method to accelerate multi-objective Bayesian optimisation by integrating Gaussian process predictive gradients as auxiliary signals for faster convergence. The approach augments existing acquisition functions with local stationarity information rather than replacing them, demonstrating significant speedups on stationary problems when surrogate models are accurate. While effective for specific benchmark scenarios, the technique relies heavily on the accuracy of the underlying surrogates, limiting its immediate applicability to highly non-stationary real-world tasks.
Accelerating Multi-Objective Bayesian Optimisation via Predictive-Gradient Catalysts
arXiv:2606.06984v1 Announce Type: new 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.
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.
Machine Learning (cs.LG)
arXiv:2606.06984 [cs.LG]
(or arXiv:2606.06984v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.06984 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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