SymboLLM-FE combines symbolic regression and large language models to automate feature engineering for tabular data. It first extracts mathematically expressive formulas that correlate strongly with the target, then refines them with LLMs to improve interpretability. Experiments on six real‑world datasets and four Kaggle competitions show that SymboLLM‑FE outperforms existing AutoFE methods while reducing the number of costly LLM calls.
By Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou, Yu-Feng Li, Lan-Zhe Guo
arXiv:2609.24126v1 Announce Type: cross
Abstract: Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important fe...
By Xuhui Liu, Lili Zheng
arXiv:2606. 17660v1 Announce Type: cross Abstract: Fine-tuning large language models (LLMs) is compute-intensive and error-prone: model performance depends sensitively on data quality and hyperparameter choices, and na\"ive runs can even degrade model performance.
By Yuxiang Luo, Haonan Long, Chen Wang, Qiqi Duan, Xiaotian Lin, Yanwei Xu, Yuyu Luo, Weikai Yang, Nan Tang
arXiv:2607. 27389v1 Announce Type: new Abstract: Learning-to-optimize (L2O) methods accelerate repeated optimization by training models to predict solutions, warm starts, branching decisions, or other forms of solver guidance.
By Bingheng Li, Junyang Cai, Yupeng Zhang, Bistra Dilkina, Jayant Kalagnanam, Dzung T. Phan
arXiv:2608. 05207v1 Announce Type: new Abstract: Frozen pretrained forecasters often fail in structured, recurring ways that are costly to repair through fine-tuning.
By Fangxin Wang, Ziyi Zhang, Diyi Zhuang, Langzhou He, Shiyu Wang, Baichuan Mo, Philip S. Yu
arXiv:2608.27704v1 Announce Type: new
Abstract: When machine learning classifiers are retrained, inputs correctly classified by the previous model version may be misclassified by the updated version,...
By Madhusudan Srinivasan, Namith Nishal Raphae