1 citations · 1 across the 7 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
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…
cs.LG2024
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…
cs.LG2023★ 1 cited
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…