# On Batch Bayesian Optimization

arXiv: https://arxiv.org/abs/1911.01032 · HTML: https://mlsanity.com/paper/1911.01032/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

## Built on by (most cited first, 12 of 12)

- [Bayesian Optimization under Stochastic Delayed Feedback](https://mlsanity.com/paper/2206.09341/) (2022)
- [Delayed Feedback in Kernel Bandits](https://mlsanity.com/paper/2302.00392/) (2023)
- [Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Design](https://mlsanity.com/paper/2306.14348/) (2023)
- [Batch Bayesian Optimization for Replicable Experimental Design](https://mlsanity.com/paper/2311.01195/) (2023)
- [Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm](https://mlsanity.com/paper/2410.23498/) (2024)
- [TS-RSR: A provably efficient approach for batch Bayesian Optimization](https://mlsanity.com/paper/2403.04764/) (2024)
- [Bayesian Optimisation for Sequential Experimental Design with Applications in Additive Manufacturing](https://mlsanity.com/paper/2107.12809/) (2021)
- [Randomized Kriging Believer for Parallel Bayesian Optimization with Regret Bounds](https://mlsanity.com/paper/2603.01470/) (2026)
- [Improved Regret Analysis for Parallel Gaussian Process Bandit Optimization](https://mlsanity.com/paper/2608.16492/) (2026)
- [The Traveling Bandit: A Framework for Bayesian Optimization with Movement Costs](https://mlsanity.com/paper/2410.14533/) (2024)
- [Multi-Variable Batch Bayesian Optimization in Materials Research: Synthetic Data Analysis of Noise Sensitivity and Problem Landscape Effects](https://mlsanity.com/paper/2504.03943/) (2025)
- [A Human-in-the-Loop Bayesian Optimization Framework for Constraint-Aware Bioprocess Development](https://mlsanity.com/paper/2606.19230/) (2026)

## 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.