# Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation

arXiv: https://arxiv.org/abs/1901.10452 · HTML: https://mlsanity.com/paper/1901.10452/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2019.

## Built on by (most cited first, 17 of 17)

- [GIBBON: General-purpose Information-Based Bayesian OptimisatioN](https://mlsanity.com/paper/2102.03324/) (2021)
- [Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization](https://mlsanity.com/paper/2211.06149/) (2022)
- [SnAKe: Bayesian Optimization with Pathwise Exploration](https://mlsanity.com/paper/2202.00060/) (2022)
- [Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale](https://mlsanity.com/paper/2201.06834/) (2022)
- [LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithms](https://mlsanity.com/paper/2505.21034/) (2025)
- [A portfolio approach to massively parallel Bayesian optimization](https://mlsanity.com/paper/2110.09334/) (2021)
- [Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL](https://mlsanity.com/paper/2106.15883/) (2021)
- [DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture](https://mlsanity.com/paper/2109.05765/) (2021)
- [Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization](https://mlsanity.com/paper/2207.00479/) (2022)
- [A penalisation method for batch multi-objective Bayesian optimisation with application in heat exchanger design](https://mlsanity.com/paper/2206.13326/) (2022)
- [Batch Bayesian Optimization via Particle Gradient Flows](https://mlsanity.com/paper/2209.04722/) (2022)
- [A Unified Gaussian Process for Branching and Nested Hyperparameter Optimization](https://mlsanity.com/paper/2402.04885/) (2024)
- [Scalable Batch Bayesian Optimization Via Subspace Acquisition Functions](https://mlsanity.com/paper/2411.16206/) (2024)
- [Standard Acquisition Is Sufficient for Asynchronous Bayesian Optimization](https://mlsanity.com/paper/2603.13501/) (2026)
- [ALMAB-DC: Active Learning, Multi-Armed Bandits, and Distributed Computing for Sequential Experimental Design and Black-Box Optimization](https://mlsanity.com/paper/2603.21180/) (2026)
- [mlr3mbo: Bayesian Optimization in R](https://mlsanity.com/paper/2603.29730/) (2026)
- [Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not](https://mlsanity.com/paper/2605.18819/) (2026)

## Abstract (arXiv)

Batch Bayesian optimisation (BO) has been successfully applied to hyperparameter tuning using parallel computing, but it is wasteful of resources: workers that complete jobs ahead of others are left idle. We address this problem by developing an approach, Penalising Locally for Asynchronous Bayesian Optimisation on k workers (PLAyBOOK), for asynchronous parallel BO. We demonstrate empirically the efficacy of PLAyBOOK and its variants on synthetic tasks and a real-world problem. We undertake a comparison between synchronous and asynchronous BO, and show that asynchronous BO often outperforms synchronous batch BO in both wall-clock time and number of function evaluations.