Yongfan Lu — accepted AI-conference papers

2025

Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models

Bingdong Li, Zixiang Di, Yongfan Lu, Hong Qian, Feng Wang, Peng Yang +2

AAAI 2025technical

Multi-objective Bayesian optimization (MOBO) has shown promising performance on various expensive multi-objective optimization problems (EMOPs). However, effectively modeling complex distributions of the Pareto optimal solutions is difficult with limited function evaluations. Existing Pareto set lea…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2024

Are You Concerned about Limited Function Evaluations: Data-Augmented Pareto Set Learning for Expensive Multi-Objective Optimization

Yongfan Lu, Bingdong Li, Aimin Zhou

AAAI 2024technical

Optimizing multiple conflicting black-box objectives simultaneously is a prevalent occurrence in many real-world applications, such as neural architecture search, and machine learning. These problems are known as expensive multi-objective optimization problems (EMOPs) when the function evaluations a…

Cited by 6 Source ↗PDF ↗code ↗Scholar ↗