6 papers
Meta-LinEXP3: Online-within-Online Learning for Adversarial Linear Contextual Bandits
Hao Li, Jie Xu, Zheng Xie
Meta-learning has emerged as an effective paradigm for transferring knowledge across sequential bandit tasks. While substantial progress has been made for stochastic bandits and no…
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…
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…
Modified Meta-Thompson Sampling for Linear Bandits and Its Bayes Regret Analysis
Hao Li, Dong Liang, Zheng Xie
Meta-learning is characterized by its ability to learn how to learn, enabling the adaptation of learning strategies across different tasks. Recent research introduced the Meta-Thom…
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…
Fast Sampling for Linear Inverse Problems of Vectors and Tensors using Multilinear Extensions
Hao Li, Dong Liang, Zixi Zhou +1
This paper studies the problem of sampling vector and tensor signals, which is the process of choosing sites in vectors and tensors to place sensors for better recovery. A small co…