6 papers
Fine-Tuning Impairs the Balancedness of Foundation Models in Long-tailed Personalized Federated Learning
Shihao Hou, Chikai Shang, Zhiheng Yang +5
Personalized federated learning (PFL) with foundation models has emerged as a promising paradigm enabling clients to adapt to heterogeneous data distributions. However, real-world…
Decision Boundary-aware Generation for Long-tailed Learning
Jiacheng Yang, Ruichi Zhang, Chikai Shang +5
Long-tailed data bias decision boundaries toward head classes and degrade tail class accuracy. Diffusion-based generative augmentation address this problem by generating additional…
CUE: Concept-Aware Multi-Label Expansion to Mitigate Concept Confusion in Long-Tailed Learning
Ruichi Zhang, Chikai Shang, Jiacheng Yang +4
Long-tailed distributions are common in real-world recognition tasks, where a few head classes have many samples while most tail classes have very few. Recently, fine-tuning founda…
The Semantic Architect: How FEAML Bridges Structured Data and LLMs for Multi-Label Tasks
Wanfu Gao, Zebin He, Jun Gao
Existing feature engineering methods based on large language models (LLMs) have not yet been applied to multi-label learning tasks. They lack the ability to model complex label dep…
Graph Random Walk with Feature-Label Space Alignment: A Multi-Label Feature Selection Method
Wanfu Gao, Jun Gao, Qingqi Han +2
The rapid growth in feature dimension may introduce implicit associations between features and labels in multi-label datasets, making the relationships between features and labels…
Two-Stage Feature Generation with Transformer and Reinforcement Learning
Wanfu Gao, Zengyao Man, Zebin He +3
Feature generation is a critical step in machine learning, aiming to enhance model performance by capturing complex relationships within the data and generating meaningful new feat…