Chengyu LU

2 accepted papers

2025

Multi-Objective Neural Bandits with Random Scalarization

Ji Cheng, Bo Xue, Chengyu Lu, Ziqiang Cui, Qingfu Zhang

IJCAI 2025

Multi-objective multi-armed bandit (MOMAB) problems are crucial for complex decision-making scenarios where multiple conflicting objectives must be simultaneously optimized. However, most existing works are based on the linear assumption of the feedback rewards, which significantly constrains their

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2025

Neural Evolution Strategy for Black-box Pareto Set Learning

Chengyu LU, Zhenhua Li, Xi Lin, Ji Cheng, Qingfu Zhang

NeurIPS 2025poster

Multi-objective optimization problems (MOPs) are prevalent in numerous real-world applications. Recently, Pareto Set Learning (PSL) has emerged as a powerful paradigm for solving MOPs. PSL can produce a neural network for modeling the set of all Pareto optimal solutions. However, applying PSL to bla…

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