Agentic Bayesian Optimization through Surrogate-Augmented Autoresearch
Paul Brunzema, Louis Tiao, Nhat Le, Kevin De Angeli, Yao Xuan, Djordje Gligorijevic
cs.LG, stat.ML
Submitted: 2026-08-11
Updated: 2026-08-13
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
- Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization
- Can LLMs Beat Classical Hyperparameter Optimization Algorithms? A Study on autoresearch
- A Tutorial on Bayesian Optimization
- Bayesian Optimization with Unknown Constraints
- LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet?
- The CMA Evolution Strategy: A Tutorial
- Training Compute-Optimal Large Language Models
- MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
- Language Models (Mostly) Know What They Know
- LILO: Bayesian Optimization with Natural Language Feedback
- Large Language Model Agent for Hyper-Parameter Optimization
- Large Language Models to Enhance Bayesian Optimization
- The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
- ChemCrow: Augmenting large-language models with chemistry tools
- OptiMindTune: A Multi-Agent Framework for Intelligent Hyperparameter Optimization
- Sequential Large Language Model-Based Hyper-parameter Optimization
- Large language models as uncertainty-calibrated optimizers for experimental discovery
- Improving LLM-based Global Optimization with Search Space Partitioning
- Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs
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