# Preference-Aware Constrained Multi-Objective Bayesian Optimization

arXiv: https://arxiv.org/abs/2303.13034 · HTML: https://mlsanity.com/paper/2303.13034/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 82 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 24 of 24)

- [Scalable Global Optimization via Local Bayesian Optimization](https://mlsanity.com/paper/1910.01739/) (2019)
- [Predictive Entropy Search for Efficient Global Optimization of Black-box Functions](https://mlsanity.com/paper/1406.2541/) (2014)
- [Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2006.05078/) (2020)
- [Predictive Entropy Search for Multi-objective Bayesian Optimization](https://mlsanity.com/paper/1511.05467/) (2015)
- [Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces](https://mlsanity.com/paper/2109.10964/) (2021)
- [Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints](https://mlsanity.com/paper/2009.01721/) (2020)
- [Multi-objective Bayesian Optimization using Pareto-frontier Entropy](https://mlsanity.com/paper/1906.00127/) (2019)
- [Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2204.05944/) (2022)
- [Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes](https://mlsanity.com/paper/2203.11382/) (2022)
- [Robust Multi-Objective Bayesian Optimization Under Input Noise](https://mlsanity.com/paper/2202.07549/) (2022)
- [Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints](https://mlsanity.com/paper/1609.01051/) (2016)
- [A Bayesian approach to constrained single- and multi-objective optimization](https://mlsanity.com/paper/1510.00503/) (2015)
- [Multi-Fidelity Multi-Objective Bayesian Optimization: An Output Space Entropy Search Approach](https://mlsanity.com/paper/2011.01542/) (2020)
- [Multi-objective Bayesian optimisation with preferences over objectives](https://mlsanity.com/paper/1902.04228/) (2019)
- [Improved Max-value Entropy Search for Multi-objective Bayesian Optimization with Constraints](https://mlsanity.com/paper/2011.01150/) (2020)
- [Output Space Entropy Search Framework for Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2110.06980/) (2021)
- [Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise](https://mlsanity.com/paper/2109.05437/) (2021)
- [Learning-based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems](https://mlsanity.com/paper/1810.08869/) (2018)
- [Bayesian Optimization Approach for Analog Circuit Synthesis Using Neural Network](https://mlsanity.com/paper/1912.00402/) (2019)
- [An Energy-Aware Online Learning Framework for Resource Management in Heterogeneous Platforms](https://mlsanity.com/paper/2003.09526/) (2020)
- [Design-Space Exploration and Optimization of an Energy-Efficient and Reliable 3D Small-world Network-on-Chip](https://mlsanity.com/paper/1608.06972/) (2016)
- [Machine Learning and Manycore Systems Design: A Serendipitous Symbiosis](https://mlsanity.com/paper/1712.00076/) (2017)
- [Uncertainty aware Search Framework for Multi-Objective Bayesian Optimization with Constraints](https://mlsanity.com/paper/2008.07029/) (2020)
- [R-MBO: A Multi-surrogate Approach for Preference Incorporation in Multi-objective Bayesian Optimisation](https://mlsanity.com/paper/2204.13166/) (2022)

## Built on by (most cited first, 4 of 4)

- [Multi-Objective Bayesian Optimization with Active Preference Learning](https://mlsanity.com/paper/2311.13460/) (2023)
- [User Preference Meets Pareto-Optimality in Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2502.06971/) (2025)
- [Vector Optimization with Gaussian Process Bandits](https://mlsanity.com/paper/2412.02484/) (2024)
- [Non-Myopic Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2412.08085/) (2024)

## Abstract (arXiv)

This paper addresses the problem of constrained multi-objective optimization over black-box objective functions with practitioner-specified preferences over the objectives when a large fraction of the input space is infeasible (i.e., violates constraints). This problem arises in many engineering design problems including analog circuits and electric power system design. Our overall goal is to approximate the optimal Pareto set over the small fraction of feasible input designs. The key challenges include the huge size of the design space, multiple objectives and large number of constraints, and the small fraction of feasible input designs which can be identified only after performing expensive simulations. We propose a novel and efficient preference-aware constrained multi-objective Bayesian optimization approach referred to as PAC-MOO to address these challenges. The key idea is to learn surrogate models for both output objectives and constraints, and select the candidate input for evaluation in each iteration that maximizes the information gained about the optimal constrained Pareto front while factoring in the preferences over objectives. Our experiments on two real-world analog circuit design optimization problems demonstrate the efficacy of PAC-MOO over prior methods.