# A portfolio approach to massively parallel Bayesian optimization

arXiv: https://arxiv.org/abs/2110.09334 · HTML: https://mlsanity.com/paper/2110.09334/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 111 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 28 of 28)

- [Scalable Global Optimization via Local Bayesian Optimization](https://mlsanity.com/paper/1910.01739/) (2019)
- [Max-value Entropy Search for Efficient Bayesian Optimization](https://mlsanity.com/paper/1703.01968/) (2017)
- [Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2006.05078/) (2020)
- [Exact Gaussian Processes on a Million Data Points](https://mlsanity.com/paper/1903.08114/) (2019)
- [Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement](https://mlsanity.com/paper/2105.08195/) (2021)
- [Batched Large-scale Bayesian Optimization in High-dimensional Spaces](https://mlsanity.com/paper/1706.01445/) (2017)
- [Maximizing acquisition functions for Bayesian optimization](https://mlsanity.com/paper/1805.10196/) (2018)
- [Batch Bayesian Optimization via Local Penalization](https://mlsanity.com/paper/1505.08052/) (2015)
- [Constrained Bayesian Optimization with Noisy Experiments](https://mlsanity.com/paper/1706.07094/) (2017)
- [A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations](https://mlsanity.com/paper/1805.12168/) (2018)
- [Scalable Constrained Bayesian Optimization](https://mlsanity.com/paper/2002.08526/) (2020)
- [Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space](https://mlsanity.com/paper/1706.01825/) (2017)
- [The Parallel Knowledge Gradient Method for Batch Bayesian Optimization](https://mlsanity.com/paper/1606.04414/) (2016)
- [BOCK : Bayesian Optimization with Cylindrical Kernels](https://mlsanity.com/paper/1806.01619/) (2018)
- [Bayesian Optimization with Gradients](https://mlsanity.com/paper/1703.04389/) (2017)
- [Greed is Good: Exploration and Exploitation Trade-offs in Bayesian Optimisation](https://mlsanity.com/paper/1911.12809/) (2019)
- [GIBBON: General-purpose Information-Based Bayesian OptimisatioN](https://mlsanity.com/paper/2102.03324/) (2021)
- [Parallel Bayesian Global Optimization of Expensive Functions](https://mlsanity.com/paper/1602.05149/) (2016)
- [Practical heteroskedastic Gaussian process modeling for large simulation experiments](https://mlsanity.com/paper/1611.05902/) (2016)
- [Replication or exploration? Sequential design for stochastic simulation experiments](https://mlsanity.com/paper/1710.03206/) (2017)
- [Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation](https://mlsanity.com/paper/1901.10452/) (2019)
- [mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions](https://mlsanity.com/paper/1703.03373/) (2017)
- [Differentiating the multipoint Expected Improvement for optimal batch design](https://mlsanity.com/paper/1503.05509/) (2015)
- [The Kalai-Smorodinski solution for many-objective Bayesian optimization](https://mlsanity.com/paper/1902.06565/) (2019)
- [Bayesian Optimization Allowing for Common Random Numbers](https://mlsanity.com/paper/1910.09259/) (2019)
- [Efficient and Scalable Batch Bayesian Optimization Using K-Means](https://mlsanity.com/paper/1806.01159/) (2018)
- [Microsimulation Model Calibration using Incremental Mixture Approximate Bayesian Computation](https://mlsanity.com/paper/1804.02090/) (2018)
- [Analyzing Stochastic Computer Models: A Review with Opportunities](https://mlsanity.com/paper/2002.01321/) (2020)

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

- [Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow](https://mlsanity.com/paper/2302.08436/) (2023)
- [Trajectory-oriented optimization of stochastic epidemiological models](https://mlsanity.com/paper/2305.03926/) (2023)
- [Optimal Initialization of Batch Bayesian Optimization](https://mlsanity.com/paper/2404.17997/) (2024)
- [Gearing Gaussian process modeling and sequential design towards stochastic simulators](https://mlsanity.com/paper/2412.07306/) (2024)
- [SCORE: A 1D Reparameterization Technique to Break Bayesian Optimization's Curse of Dimensionality](https://mlsanity.com/paper/2406.12661/) (2024)
- [Scalable Batch Bayesian Optimization Via Subspace Acquisition Functions](https://mlsanity.com/paper/2411.16206/) (2024)
- [Advancing calibration for stochastic agent-based models in epidemiology with Stein variational inference and Gaussian process surrogates](https://mlsanity.com/paper/2502.19550/) (2025)
- [Adaptive Replication Strategies in Trust-Region-Based Bayesian Optimization of Stochastic Functions](https://mlsanity.com/paper/2504.20527/) (2025)
- [Is Sequence Information All You Need for Bayesian Optimization of Antibodies?](https://mlsanity.com/paper/2509.24933/) (2025)

## Abstract (arXiv)

One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayesian optimization have been proposed. They work by building a surrogate model of the black-box to simultaneously select multiple designs via an infill criterion. Still, despite the increased availability of computing resources that enable large-scale parallelism, the strategies that work for selecting a few tens of parallel designs for evaluations become limiting due to the complexity of selecting more designs. It is even more crucial when the black-box is noisy, necessitating more evaluations as well as repeating experiments. Here we propose a scalable strategy that can keep up with massive batching natively, focused on the exploration/exploitation trade-off and a portfolio allocation. We compare the approach with related methods on noisy functions, for mono and multi-objective optimization tasks. These experiments show orders of magnitude speed improvements over existing methods with similar or better performance.