mlsanityData to 7 Oct 2026

Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout

arXiv 1911.01004 ↗First posted 4 Nov 2019cs.LG math.OC stat.MLcited by 23

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Abstract (arXiv)

Lookahead, also known as non-myopic, Bayesian optimization (BO) aims to find optimal sampling policies through solving a dynamic program (DP) that maximizes a long-term reward over a rolling horizon. Though promising, lookahead BO faces the risk of error propagation through its increased dependence on a possibly mis-specified model. In this work we focus on the rollout approximation for solving the intractable DP. We first prove the improving nature of rollout in tackling lookahead BO and provide a sufficient condition for the used heuristic to be rollout improving. We then provide both a theoretical and practical guideline to decide on the rolling horizon stagewise. This guideline is built on quantifying the negative effect of a mis-specified model. To illustrate our idea, we provide case studies on both single and multi-information source BO. Empirical results show the advantageous properties of our method over several myopic and non-myopic BO algorithms.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Nov 2019.

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Citing papers per year: 2021: 7; 2022: 3; 2023: 5; 2024: 5; 2025: 2; 2026: 1.

23 papers.

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  1. 2022Recent Advances in Bayesian Optimizationcited by 74
  2. 2021Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimizationcited by 22
  3. 2021Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costscited by 18
  4. 2024Cost-aware Bayesian Optimization via the Pandora's Box Gittins Indexcited by 16
  5. 2022SnAKe: Bayesian Optimization with Pathwise Explorationcited by 14
  6. 2023Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Designcited by 9
  7. 2021Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modelingcited by 8
  8. 2022Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approachcited by 8
  9. 2023MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learningcited by 5
  10. 2023Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimatorscited by 5
  11. 2021HyperJump: Accelerating HyperBand via Risk Modellingcited by 4
  12. 2021Towards Futuristic Autonomous Experimentation--A Surprise-Reacting Sequential Experiment Policycited by 4
11 more
  1. 2024Accelerating Look-ahead in Bayesian Optimization: Multilevel Monte Carlo is All you Needcited by 4
  2. 2021Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefitscited by 3
  3. 2025A Guide to Bayesian Optimization in Bioprocess Engineeringcited by 1
  4. 2021Adaptive Sampling using POMDPs with Domain-Specific Considerationscited by 0
  5. 2023Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structurecited by 0
  6. 2023Deterministic Langevin Unconstrained Optimization with Normalizing Flowscited by 0
  7. 2024Information-Theoretic Safe Bayesian Optimizationcited by 0
  8. 2024The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costscited by 0
  9. 2024Nonmyopic Global Optimisation via Approximate Dynamic Programmingcited by 0
  10. 2025Model-free Reinforcement Learning for Model-based Control: Towards Safe, Interpretable and Sample-efficient Agentscited by 0
  11. 2026Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settingscited by 0