# Uncertainty-Aware Search Framework for Multi-Objective Bayesian Optimization

arXiv: https://arxiv.org/abs/2204.05944 · HTML: https://mlsanity.com/paper/2204.05944/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 12 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [Predictive Entropy Search for Multi-objective Bayesian Optimization](https://mlsanity.com/paper/1511.05467/) (2015)
- [Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints](https://mlsanity.com/paper/2009.01721/) (2020)
- [Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction](https://mlsanity.com/paper/1310.0732/) (2013)
- [Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimization](https://mlsanity.com/paper/1705.06808/) (2017)

## Built on by (most cited first, 30 of 44)

- [Recent Advances in Bayesian Optimization](https://mlsanity.com/paper/2206.03301/) (2022)
- [Pareto Set Learning for Expensive Multi-Objective Optimization](https://mlsanity.com/paper/2210.08495/) (2022)
- [Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond](https://mlsanity.com/paper/2501.10945/) (2025)
- [Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization](https://mlsanity.com/paper/2205.08835/) (2022)
- [TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion](https://mlsanity.com/paper/2509.25171/) (2025)
- [Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2406.08799/) (2024)
- [Federated Multi-Objective Learning](https://mlsanity.com/paper/2310.09866/) (2023)
- [Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models](https://mlsanity.com/paper/2405.08674/) (2024)
- [BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL](https://mlsanity.com/paper/2505.21974/) (2025)
- [OpenBox: A Python Toolkit for Generalized Black-box Optimization](https://mlsanity.com/paper/2304.13339/) (2023)
- [On the role of Model Uncertainties in Bayesian Optimization](https://mlsanity.com/paper/2301.05983/) (2023)
- [Constrained Multi-objective Bayesian Optimization through Optimistic Constraints Estimation](https://mlsanity.com/paper/2411.03641/) (2024)
- [Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty](https://mlsanity.com/paper/2301.11588/) (2023)
- [Risk-Controlling Model Selection via Guided Bayesian Optimization](https://mlsanity.com/paper/2312.01692/) (2023)
- [PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization](https://mlsanity.com/paper/2412.10961/) (2024)
- [Preference-Aware Constrained Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2303.13034/) (2023)
- [Improving Pareto Set Learning for Expensive Multi-objective Optimization via Stein Variational Hypernetworks](https://mlsanity.com/paper/2412.17312/) (2024)
- [Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow](https://mlsanity.com/paper/2311.13159/) (2023)
- [Federated Communication-Efficient Multi-Objective Optimization](https://mlsanity.com/paper/2410.16398/) (2024)
- [Multi-Objective Causal Bayesian Optimization](https://mlsanity.com/paper/2502.14755/) (2025)
- [MOBO-OSD: Batch Multi-Objective Bayesian Optimization via Orthogonal Search Directions](https://mlsanity.com/paper/2510.20872/) (2025)
- [KATO: Knowledge Alignment and Transfer for Transistor Sizing of Different Design and Technology](https://mlsanity.com/paper/2404.14433/) (2024)
- [Large Language Model-Driven Surrogate-Assisted Evolutionary Algorithm for Expensive Optimization](https://mlsanity.com/paper/2507.02892/) (2025)
- [FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization](https://mlsanity.com/paper/2509.03244/) (2025)
- [Exploiting Function-Family Structure in Analog Circuit Optimization](https://mlsanity.com/paper/2512.00712/) (2025)
- [Do We Really Need to Approach the Entire Pareto Front in Many-Objective Bayesian Optimisation?](https://mlsanity.com/paper/2604.09417/) (2026)
- [HyperTuner: A Cross-Layer Multi-Objective Hyperparameter Auto-Tuning Framework for Data Analytic Services](https://mlsanity.com/paper/2304.10051/) (2023)
- [Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization](https://mlsanity.com/paper/2306.01095/) (2023)
- [Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Exploration](https://mlsanity.com/paper/2408.11751/) (2024)
- [Preferential Multi-Objective Bayesian Optimization for Drug Discovery](https://mlsanity.com/paper/2503.16841/) (2025)

## Abstract (arXiv)

We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions while minimizing the number of function evaluations. For example, in hardware design optimization, we need to find the designs that trade-off performance, energy, and area overhead using expensive simulations. We propose a novel uncertainty-aware search framework referred to as USeMO to efficiently select the sequence of inputs for evaluation to solve this problem. The selection method of USeMO consists of solving a cheap MO optimization problem via surrogate models of the true functions to identify the most promising candidates and picking the best candidate based on a measure of uncertainty. We also provide theoretical analysis to characterize the efficacy of our approach. Our experiments on several synthetic and six diverse real-world benchmark problems show that USeMO consistently outperforms the state-of-the-art algorithms.