From 74041bd5cb3d8a0b9e0e2f8c45b421510c5558fa Mon Sep 17 00:00:00 2001 From: Cyzus Chi Date: Wed, 30 Oct 2024 15:56:06 +0800 Subject: [PATCH] update readme intro --- metagpt/ext/sela/README.md | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/metagpt/ext/sela/README.md b/metagpt/ext/sela/README.md index a942fdb7d..d0fbcf4b8 100644 --- a/metagpt/ext/sela/README.md +++ b/metagpt/ext/sela/README.md @@ -1,5 +1,7 @@ # SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning +SELA is an innovative framework that enhances Automated Machine Learning (AutoML) by integrating Monte Carlo Tree Search (MCTS) with LLM-based agents. Traditional AutoML methods often generate low-diversity and suboptimal code, limiting their effectiveness in model selection and ensembling. SELA addresses these challenges by representing pipeline configurations as trees, enabling agents to intelligently explore the solution space and iteratively refine their strategies based on experimental feedback. + ## 1. Data Preparation You can either download the datasets from the link or prepare the datasets from scratch. @@ -82,4 +84,18 @@ ### Ablation Study - **Use a set of insights:** ```bash python run_experiment.py --exp_mode rs --task titanic --rs_mode set - ``` \ No newline at end of file + ``` + +## 4. Citation + +```bibtex +@misc{chi2024selatreesearchenhancedllm, + title={SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning}, + author={Yizhou Chi and Yizhang Lin and Sirui Hong and Duyi Pan and Yaying Fei and Guanghao Mei and Bangbang Liu and Tianqi Pang and Jacky Kwok and Ceyao Zhang and Bang Liu and Chenglin Wu}, + year={2024}, + eprint={2410.17238}, + archivePrefix={arXiv}, + primaryClass={cs.AI}, + url={https://arxiv.org/abs/2410.17238}, +} +```