Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization
The paper introduces a novel framework called Kernel Discovery for high-dimensional Bayesian optimization. This framework utilizes a two-stage approach driven by large language models (LLMs) to explore a broader kernel space without conditioning on observations. The proposed method outperforms existing baselines on multiple benchmarks, achieving an average rank of 1.2 out of 17.
- ▪Kernel Discovery is an LLM-driven evolutionary framework for high-dimensional Bayesian optimization.
- ▪The method does not require conditioning on observations, addressing limitations of existing automated approaches.
- ▪On five high-dimensional benchmarks, the proposed method achieved an average rank of 1.2 out of 17, outperforming competitive baselines.
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Computer Science > Machine Learning arXiv:2605.20249 (cs) [Submitted on 18 May 2026] Title:Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization Authors:Taeyoung Yun, Woocheol Shin, Inhyuck Song, Jaewoo Lee, Jinkyoo Park View a PDF of the paper titled Automated Kernel Discovery Towards Understanding High-dimensional Bayesian Optimization, by Taeyoung Yun and 4 other authors View PDF HTML (experimental) 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.
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