1 citations · 1 across the 3 of their papers we have counts for
6 papers
Adaptive Protection for Evolutionary Feature Construction in Symbolic Regression with Application to Credit Classification
Hengzhe Zhang, Qi Chen, Bing Xue +3
Evolutionary feature construction has shown strong promise in symbolic regression by automatically discovering informative transformations of input features that enhance a simple b…
Benchmarking Zero-Shot LLM-Generated Parent Selection in Genetic Programming for Symbolic Regression
Hengzhe Zhang, Qi Chen, Bing Xue +2
Parent selection significantly affects exploration, exploitation, and complexity control in genetic programming (GP) for symbolic regression. It is unclear whether large language m…
Enhancing Generalization in Evolutionary Feature Construction for Symbolic Regression through Vicinal Jensen Gap Minimization
Hengzhe Zhang, Qi Chen, Bing Xue +2
Genetic programming-based feature construction has achieved significant success in recent years as an automated machine learning technique to enhance learning performance. However,…
LLM-Meta-SR: In-Context Learning for Evolving Selection Operators in Symbolic Regression
Hengzhe Zhang, Qi Chen, Bing Xue +2
Large language models (LLMs) have revolutionized algorithm development, yet their application in symbolic regression, where algorithms automatically discover symbolic expressions f…
Meta-Learning Neural Procedural Biases
Christian Raymond, Qi Chen, Bing Xue +1
The goal of few-shot learning is to generalize and achieve high performance on new unseen learning tasks, where each task has only a limited number of examples available. Gradient-…
Sharpness-Aware Minimization for Evolutionary Feature Construction in Regression
Hengzhe Zhang, Qi Chen, Bing Xue +2
In recent years, genetic programming (GP)-based evolutionary feature construction has achieved significant success. However, a primary challenge with evolutionary feature construct…