mlsanityData to 14 Oct 2026

Warm Starting Bayesian Optimization

arXiv 1608.03585 ↗First posted 11 Aug 2016stat.ML cs.LG stat.APcited by 21

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Abstract (arXiv)

We develop a framework for warm-starting Bayesian optimization, that reduces the solution time required to solve an optimization problem that is one in a sequence of related problems. This is useful when optimizing the output of a stochastic simulator that fails to provide derivative information, for which Bayesian optimization methods are well-suited. Solving sequences of related optimization problems arises when making several business decisions using one optimization model and input data collected over different time periods or markets. While many gradient-based methods can be warm started by initiating optimization at the solution to the previous problem, this warm start approach does not apply to Bayesian optimization methods, which carry a full metamodel of the objective function from iteration to iteration. Our approach builds a joint statistical model of the entire collection of related objective functions, and uses a value of information calculation to recommend points to evaluate.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Aug 2016.

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Citing papers per year: 2021: 3; 2022: 3; 2023: 1; 2024: 5; 2025: 5; 2026: 4.

21 papers.

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  1. 2024Large Language Models to Enhance Bayesian Optimizationcited by 135
  2. 2022Towards Learning Universal Hyperparameter Optimizers with Transformerscited by 81
  3. 2021Pre-trained Gaussian Processes for Bayesian Optimizationcited by 63
  4. 2023Transfer Learning for Bayesian Optimization: A Surveycited by 27
  5. 2024Sequential Large Language Model-Based Hyper-parameter Optimizationcited by 24
  6. 2025Language-Based Bayesian Optimization Research Assistant (BORA)cited by 15
  7. 2024Reinforced In-Context Black-Box Optimizationcited by 14
  8. 2021JUMBO: Scalable Multi-task Bayesian Optimization using Offline Datacited by 8
  9. 2022On Provably Robust Meta-Bayesian Optimizationcited by 8
  10. 2022Pre-training helps Bayesian optimization toocited by 5
  11. 2024Improving Hyperparameter Optimization with Checkpointed Model Weightscited by 5
  12. 2025ADMIRE-BayesOpt: Accelerated Data MIxture RE-weighting for Language Models with Bayesian Optimizationcited by 5
9 more
  1. 2021Amortized Auto-Tuning: Cost-Efficient Bayesian Transfer Optimization for Hyperparameter Recommendationcited by 4
  2. 2025None To Optima in Few Shots: Bayesian Optimization with MDP Priorscited by 3
  3. 2026Evolutionary Multi-Task Optimization for LLM-Guided Program Discoverycited by 3
  4. 2024Robust Transfer Learning for Active Level Set Estimation with Locally Adaptive Gaussian Process Priorcited by 1
  5. 2025Provable Accelerated Bayesian Optimization with Knowledge Transfercited by 1
  6. 2025Why risk matters for protein binder designcited by 0
  7. 2026An Empirical Study on Ensemble-Based Transfer Learning Bayesian Optimisation with Mixed Variable Typescited by 0
  8. 2026SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimizationcited by 0
  9. 2026Harnessing Large Language Models to Compile Task-Relevant Context into Bayesian Optimisationcited by 0