5 citations · 6 across the 5 of their papers we have counts for
5 papers
Geometric Manifold Rectification for Imbalanced Learning
Xubin Wang, Qing Li, Weijia Jia
Imbalanced classification presents a formidable challenge in machine learning, particularly when tabular datasets are plagued by noise and overlapping class boundaries. From a geom…
Meta-Sel: Efficient Demonstration Selection for In-Context Learning via Supervised Meta-Learning
Xubin Wang, Weijia Jia
Demonstration selection is a practical bottleneck in in-context learning (ICL): under a tight prompt budget, accuracy can change substantially depending on which few-shot examples…
Cognitive Edge Computing: A Comprehensive Survey on Optimizing Large Models and AI Agents for Pervasive Deployment
Xubin Wang, Qing Li, Weijia Jia
This article surveys Cognitive Edge Computing as a practical and methodical pathway for deploying reasoning-capable Large Language Models (LLMs) and autonomous AI agents on resourc…
Demonstration Selection for In-Context Learning via Reinforcement Learning
Xubin Wang, Jianfei Wu, Yichen Yuan +3
Diversity in demonstration selection is critical for enhancing model generalization by enabling broader coverage of structures and concepts. Constructing appropriate demonstration…
Enhancing Text Annotation through Rationale-Driven Collaborative Few-Shot Prompting
Jianfei Wu, Xubin Wang, Weijia Jia
The traditional data annotation process is often labor-intensive, time-consuming, and susceptible to human bias, which complicates the management of increasingly complex datasets.…