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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20249 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | u_T6ENGwN0ry |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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