Reasoning in Reasoning: A Hierarchical Framework for Neural Theorem Proving (NeurIPS 2024 Workshop MATH-AI)

Ziyu Ye, Jiacheng Chen, Jonathan Light, Yifei Wang, Jiankai Sun, Mac Schwager, Philip Torr, Guohao Li, Yuxin Chen, Kaiyu Yang, Yisong Yue, Ziniu Hu.

SYMBOL: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning (ICLR 2024)

Jiacheng Chen*, Zeyuan Ma*, Hongshu Guo, Yining Ma, Jie Zhang, Yue-Jiao Gong.

Project

• Unlike previous methods incrementally auto-configuring some existing black-box algortithms, SYMBOL directly generate stepwise update rule in the form of symbolic eqution to achieve more flexible and interpretable optimization behaviour.

MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning (Neurips 2023 Oral)

Zeyuan Ma, Hongshu Guo, Jiacheng Chen, Zhenrui Li, Guojun Peng, Yue-Jiao Gong, Yining Ma, Zhiguang Cao.

Project

• We released a benchmark platform for Meta-Black-Box Optimization named MetaBox. We integrate three different testsuits, about 20 baselines including traditional black-box methods and Meta-Black-Box methods, and new evaluation metrics tailored for Meta-Black-Box optimization. The codebase can be found here.

Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning (GECCO 2024)

Zeyuan Ma*, Jiacheng Chen*, Hongshu Guo, Yining Ma, Yue-Jiao Gong.

• We explore about how to make a trade-off between exploration and exploitation in Black-Box optimization through learn-based method. In this work, we carefully designed a framework which is based on transfromer-based model and leverage exploration-exploitation related feature tailored for black-box optimization scenario to resolve this problem.

Neural Exploratory Landscape Analysis

Zeyuan Ma, Jiacheng Chen, Hongshu Guo, Yue-Jiao Gong.

• We developed an Neural-Network based lanscape analyser to replace the feature-extracting parts in Meta-Black-Box works which is usually manually designed. To ensure the generalization ability of the NeurELA, we let it operate in Multi-task setting and use neuroevolution to train it.

LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation

Zeyuan Ma, Hongshu Guo, Jiacheng Chen, Guojun Peng, Zhiguang Cao, Yining Ma, Yue-Jiao Gong.

• We propose to fine-tune language model to generate executable code that can be used for optimization tasks. We proposed a dataset that containing diversed optimization problems and corresponding algorithm in this paper, also leverage some tricks during training process and finally provided a fine-tuned LM for optimization tasks.