arXiv:2605.20249v1 Announce Type: new Abstract: Gaussian Process (GP) kernels are central to Bayesian optimization (BO), yet designing effective kernels for high-dimensional problems still relies on extensive manual engineering. Existing automated approaches struggle in high dimensions for two bottlenecks: their kernel search space is limited to additions and multiplications of base kernels, and LLM-based approaches require conditioning on raw observations, which becomes infeasible due to context-length limits and the difficulty of extracting meaningful patterns. We introduce \textbf{Kernel Di
The increasing complexity of AI models and the demand for more efficient high-dimensional Bayesian Optimization necessitate automated kernel discovery, as manual methods are becoming a significant bottleneck.
This development could significantly accelerate the research and application of Bayesian optimization in complex AI systems, leading to more efficient model training and resource utilization.
The reliance on manual kernel design for high-dimensional Bayesian optimization is being challenged by automated methods, potentially making the process faster and more scalable.
- · AI researchers
- · Machine learning engineers
- · Industries using optimization for design/discovery
- · AI compute infrastructure providers
- · Manual kernel designers (niche skill)
- · Inefficient AI development pipelines
Automated kernel discovery will improve the performance and applicability of Bayesian optimization in high-dimensional search spaces.
Enhanced Bayesian optimization could accelerate scientific discovery and engineering design processes across various fields.
More efficient AI model development may reduce the compute footprint for certain tasks, impacting demand for specialized hardware.
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Read at arXiv cs.LG