mlsanityData to 7 Oct 2026

On Batch Bayesian Optimization

arXiv 1911.01032 ↗First posted 4 Nov 2019cs.LG stat.MLcited by 12

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Abstract (arXiv)

We present two algorithms for Bayesian optimization in the batch feedback setting, based on Gaussian process upper confidence bound and Thompson sampling approaches, along with frequentist regret guarantees and numerical results.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Nov 2019.

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Cited by year (2026 to 7 Oct)

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

12 papers.

  1. 2022Bayesian Optimization under Stochastic Delayed Feedbackcited by 10
  2. 2023Delayed Feedback in Kernel Banditscited by 10
  3. 2023Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Designcited by 9
  4. 2023Batch Bayesian Optimization for Replicable Experimental Designcited by 7
  5. 2024Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithmcited by 6
  6. 2024TS-RSR: A provably efficient approach for batch Bayesian Optimizationcited by 4
  7. 2021Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturingcited by 3
  8. 2026Randomized Kriging Believer for Parallel Bayesian Optimization with Regret Boundscited by 2
  9. 2024The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costscited by 0
  10. 2025Multi-Variable Batch Bayesian Optimization in Materials Research: Synthetic Data Analysis of Noise Sensitivity and Problem Landscape Effectscited by 0
  11. 2026A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Developmentcited by 0
  12. 2026Improved Regret Analysis for Parallel Gaussian Process Bandit Optimizationcited by 0