Yangde Fu — accepted AI-conference papers

2026

Automated Random Embedding for Practical Bayesian Optimization with Unknown Effective Dimension

Hong Qian, Xiang Shu, Xiang Xia, Xuhui Liu, Yangde Fu, Bei Liang +2

IJCAI 2026

Bayesian optimization is widely employed for optimizing complex black-box functions but struggles with the curse of dimensionality. Random embedding, as a dimension reduction strategy, simplifies tasks that possess the effective dimension by optimizing within a low-dimensional subspace. However, det

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2026

Diversity-Driven Offline Multi-Objective Optimization via Bi-Level Pareto Set Learning

Yiyi Zhu, Yaolin Wen, Xiang Xia, Xin An, Hanyi Si, Xiang Shu +3

ICML 2026poster

Multi-objective optimization (MOO) has emerged as a powerful approach to solving complex optimization problems involving multiple objectives. In many practical scenarios, function evaluations are unavailable or prohibitively expensive, necessitating optimization solely based on a fixed offline datas…

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