5 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2025
MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization
Zeyuan Ma, Yue-Jiao Gong, Hongshu Guo +11
Meta-Black-Box Optimization (MetaBBO) streamlines the automation of optimization algorithm design through meta-learning. It typically employs a bi-level structure: the meta-level p…
cs.LG2024
: Towards Effective and Efficient Cost Function Design for Safe Reinforcement Learning via Large Language Model
Zepeng Wang, Chao Ma, Linjiang Zhou +4
Different classes of safe reinforcement learning algorithms have shown satisfactory performance in various types of safety requirement scenarios. However, the existing methods main…
cs.LG2023★ 5 cited
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning
Zeyuan Ma, Hongshu Guo, Jiacheng Chen +5
Recently, Meta-Black-Box Optimization with Reinforcement Learning (MetaBBO-RL) has showcased the power of leveraging RL at the meta-level to mitigate manual fine-tuning of low-leve…