# Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints

arXiv: https://arxiv.org/abs/2004.00601 · HTML: https://mlsanity.com/paper/2004.00601/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2020.

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

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- [ChatGPT: More than a Weapon of Mass Deception, Ethical challenges and responses from the Human-Centered Artificial Intelligence (HCAI) perspective](https://mlsanity.com/paper/2304.11215/) (2023)
- [PF2ES: Parallel Feasible Pareto Frontier Entropy Search for Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2204.05411/) (2022)
- [Many Objective Bayesian Optimization](https://mlsanity.com/paper/2107.04126/) (2021)
- [A Similarity Measure of Gaussian Process Predictive Distributions](https://mlsanity.com/paper/2101.08061/) (2021)
- [Advanced Manufacturing Configuration by Sample-efficient Batch Bayesian Optimization](https://mlsanity.com/paper/2205.11827/) (2022)
- [Non-Myopic Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2412.08085/) (2024)
- [Multi-Objective Bayesian Optimization via Adaptive varepsilon-Constraints Decomposition](https://mlsanity.com/paper/2604.15959/) (2026)
- [Learning What to Evaluate: Correlation-Aware Decoupling for Multiobjective Bayesian Optimization](https://mlsanity.com/paper/2609.32632/) (2026)

## Abstract (arXiv)

Real-world problems often involve the optimization of several objectives under multiple constraints. An example is the hyper-parameter tuning problem of machine learning algorithms. In particular, the minimization of the estimation of the generalization error of a deep neural network and at the same time the minimization of its prediction time. We may also consider as a constraint that the deep neural network must be implemented in a chip with an area below some size. Here, both the objectives and the constraint are black boxes, i.e., functions whose analytical expressions are unknown and are expensive to evaluate. Bayesian optimization (BO) methodologies have given state-of-the-art results for the optimization of black-boxes. Nevertheless, most BO methods are sequential and evaluate the objectives and the constraints at just one input location, iteratively. Sometimes, however, we may have resources to evaluate several configurations in parallel. Notwithstanding, no parallel BO method has been proposed to deal with the optimization of multiple objectives under several constraints. If the expensive evaluations can be carried out in parallel (as when a cluster of computers is available), sequential evaluations result in a waste of resources. This article introduces PPESMOC, Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints, an information-based batch method for the simultaneous optimization of multiple expensive-to-evaluate black-box functions under the presence of several constraints. Iteratively, PPESMOC selects a batch of input locations at which to evaluate the black-boxes so as to maximally reduce the entropy of the Pareto set of the optimization problem. We present empirical evidence in the form of synthetic, benchmark and real-world experiments that illustrate the effectiveness of PPESMOC.