# New Paradigms for Exploiting Parallel Experiments in Bayesian Optimization

arXiv: https://arxiv.org/abs/2210.01071 · HTML: https://mlsanity.com/paper/2210.01071/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [Scalable Bayesian Optimization Using Deep Neural Networks](https://mlsanity.com/paper/1502.05700/) (2015)
- [Multi-fidelity Bayesian Optimisation with Continuous Approximations](https://mlsanity.com/paper/1703.06240/) (2017)
- [Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning](https://mlsanity.com/paper/1903.04703/) (2019)
- [The reparameterization trick for acquisition functions](https://mlsanity.com/paper/1712.00424/) (2017)
- [COBALT: COnstrained Bayesian optimizAtion of computationaLly expensive grey-box models exploiting derivaTive information](https://mlsanity.com/paper/2105.04114/) (2021)
- [Differentiating the multipoint Expected Improvement for optimal batch design](https://mlsanity.com/paper/1503.05509/) (2015)
- [Decentralized Schemes with Overlap for Solving Graph-Structured Optimization Problems](https://mlsanity.com/paper/1810.00491/) (2018)

## Built on by (most cited first, 9 of 9)

- [Computer-Aided Multi-Objective Optimization in Small Molecule Discovery](https://mlsanity.com/paper/2210.07209/) (2022)
- [Pareto Optimization to Accelerate Multi-Objective Virtual Screening](https://mlsanity.com/paper/2310.10598/) (2023)
- [Human-Algorithm Collaborative Bayesian Optimization for Engineering Systems](https://mlsanity.com/paper/2404.10949/) (2024)
- [Multi-fidelity batch Bayesian optimization for bioprocess development across scales](https://mlsanity.com/paper/2508.10970/) (2025)
- [Survey and Tutorial of Reinforcement Learning Methods in Process Systems Engineering](https://mlsanity.com/paper/2510.24272/) (2025)
- [A Guide to Bayesian Optimization in Bioprocess Engineering](https://mlsanity.com/paper/2508.10642/) (2025)
- [Noise-Aware Optimization in Nominally Identical Manufacturing and Measuring Systems for High-Throughput Parallel Workflows](https://mlsanity.com/paper/2511.11739/) (2025)
- [Process-constrained batch Bayesian approaches for yield optimization in multi-reactor systems](https://mlsanity.com/paper/2408.02551/) (2024)
- [BITS for GAPS: Bayesian Information-Theoretic Sampling for hierarchical GAussian Process Surrogates](https://mlsanity.com/paper/2511.16815/) (2025)

## Abstract (arXiv)

Bayesian optimization (BO) is one of the most effective methods for closed-loop experimental design and black-box optimization. However, a key limitation of BO is that it is an inherently sequential algorithm (one experiment is proposed per round) and thus cannot directly exploit high-throughput (parallel) experiments. Diverse modifications to the BO framework have been proposed in the literature to enable exploitation of parallel experiments but such approaches are limited in the degree of parallelization that they can achieve and can lead to redundant experiments (thus wasting resources and potentially compromising performance). In this work, we present new parallel BO paradigms that exploit the structure of the system to partition the design space. Specifically, we propose an approach that partitions the design space by following the level sets of the performance function and an approach that exploits partially-separable structures of the performance function found. We conduct extensive numerical experiments using a reactor case study to benchmark the effectiveness of these approaches against a variety of state-of-the-art parallel algorithms reported in the literature. Our computational results show that our approaches significantly reduce the required search time and increase the probability of finding a global (rather than local) solution.