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cs.LG2025
REFN: A Reinforcement-Learning-From-Network Framework against 1-day/n-day Exploitations
Tianlong Yu, Lihong Liu, Ziyi Zhou +3
The exploitation of 1 day or n day vulnerabilities poses severe threats to networked devices due to massive deployment scales and delayed patching (average Mean Time To Patch excee…
cs.LG2024
Dynamic Evidence Decoupling for Trusted Multi-view Learning
Ying Liu, Lihong Liu, Cai Xu +3
Multi-view learning methods often focus on improving decision accuracy, while neglecting the decision uncertainty, limiting their suitability for safety-critical applications. To m…