3 papers
cs.CL2025
A Language Anchor-Guided Method for Robust Noisy Domain Generalization
Zilin Dai, Lehong Wang, Fangzhou Lin +5
Real-world machine learning applications often struggle with two major challenges: distribution shift and label noise. Models tend to overfit by focusing on redundant and uninforma…
cs.CL2024
UniAutoML: A Human-Centered Framework for Unified Discriminative and Generative AutoML with Large Language Models
Jiayi Guo, Zan Chen, Yingrui Ji +4
Automated Machine Learning (AutoML) has simplified complex ML processes such as data pre-processing, model selection, and hyper-parameter searching. However, traditional AutoML fra…
cs.LG2024
Advancing Out-of-Distribution Detection through Data Purification and Dynamic Activation Function Design
Yingrui Ji, Yao Zhu, Zhigang Li +3
In the dynamic realms of machine learning and deep learning, the robustness and reliability of models are paramount, especially in critical real-world applications. A fundamental c…