arxiv
PublishedJuly 13, 2026 at 4:00 AM
—neutral
Pitfalls and Remedies for Multi-Task Bayesian Optimization
Publisher summary· verbatim
arXiv:2607.09073v1 Announce Type: new Abstract: Bayesian optimization routinely warm-starts a target experiment with data from related source tasks, and the multi-task Gaussian process is the textbook surrogate for the job. We revisit this default in a controlled setting and find that it misestimate
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivBringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning9harxivSubagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks9harxivDistribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts9harxivIn RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning9hThe Bubble Brief
WEEKLYRead machine-learning insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗