1 citations · 1 across the 7 of their papers we have counts for
7 papers
PERO: Efficient Robust Post-Training Foundation Models for Encrypted Traffic Classification
Wumei Du, Jiarong Wen, Kaiyu Zhang +5
Encrypted traffic classification is vital for network security, yet real-world deployments are inherently sensitive to rare but high-loss errors such as misclassification of malici…
Information-Theoretic Limits of Node Localization under Hybrid Graph Positional Encodings
Zimo Yan, Zheng Xie, Chang Liu +2
Positional encoding has become a standard component in graph learning, especially for graph Transformers and other models that must distinguish structurally similar nodes, yet its…
Theoretical Investigations and Practical Enhancements on Tail Task Risk Minimization in Meta Learning
Yiqin Lv, Qi Wang, Dong Liang +1
Meta learning is a promising paradigm in the era of large models and task distributional robustness has become an indispensable consideration in real-world scenarios. Recent advanc…
Group & Reweight: A Novel Cost-Sensitive Approach to Mitigating Class Imbalance in Network Traffic Classification
Wumei Du, Dong Liang, Yiqin Lv +4
Internet services have led to the eruption of network traffic, and machine learning on these Internet data has become an indispensable tool, especially when the application is risk…
A Simple Yet Effective Strategy to Robustify the Meta Learning Paradigm
Qi Wang, Yiqin Lv, Yanghe Feng +2
Meta learning is a promising paradigm to enable skill transfer across tasks. Most previous methods employ the empirical risk minimization principle in optimization. However, the re…
Large-scale Generative Simulation Artificial Intelligence: the Next Hotspot in Generative AI
Qi Wang, Yanghe Feng, Jincai Huang +3
The concept of GenAI has been developed for decades. Until recently, it has impressed us with substantial breakthroughs in natural language processing and computer vision, actively…
Predicting publication productivity for authors: Shallow or deep architecture?
Wumei Du, Zheng Xie, Yiqin Lv
Academic administrators and funding agencies must predict the publication productivity of research groups and individuals to assess authors' abilities. However, such prediction rem…