arXiv:2607.00865v1 Announce Type: new Abstract: Bayesian Optimisation (BO) under unknown constraints is particularly challenging when feasible regions are small. In such settings, existing methods that typically rely solely on evaluations of the true objective and constraints struggle to efficiently explore the design space. However, many real-world applications offer auxiliary data sources (e.g. surrogate models or simplified simulations) that can support early exploration. Despite this potential, their integration into constrained BO remains largely unexplored. We propose a general multi-sou
The increasing complexity of AI model training and design necessitates more efficient optimisation techniques, particularly in resource-constrained environments.
This research addresses a critical limitation in Bayesian Optimisation by leveraging multiple information sources, potentially accelerating AI development and broader scientific discovery where data is scarce or expensive.
The ability to integrate auxiliary data sources into constrained Bayesian Optimisation expands the applicability of these methods to real-world problems with limited direct observation access.
- · AI/ML researchers
- · industries with complex R&D cycles
- · optimisation software providers
- · research methods relying solely on expensive direct experimentation
More efficient and cost-effective development of AI models and complex systems will become possible.
Accelerated innovation in fields like materials science, drug discovery, and engineering design through better optimisation.
Enhanced competition in applied AI as the barrier to entry for complex optimisation problems is lowered.
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Read at arXiv cs.LG