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cs.LG2026
Data Agent: Learning to Select Data via End-to-End Dynamic Optimization
Suorong Yang, Fangjian Su, Hai Gan +5
Dynamic Data selection aims to accelerate training by prioritizing informative samples during online training. However, existing methods typically rely on task-specific handcrafted…
cs.LG2026
CS-GBA: A Critical Sample-based Gradient-guided Backdoor Attack for Offline Reinforcement Learning
Yuanjie Zhao, Junnan Qiu, Yue Ding +1
Offline Reinforcement Learning (RL) enables policy optimization from static datasets but is inherently vulnerable to backdoor attacks. Existing attack strategies typically struggle…
cs.LG2025
Optimal Perturbation Budget Allocation for Data Poisoning in Offline Reinforcement Learning
Junnan Qiu, Yuanjie Zhao, Jie Li
Offline Reinforcement Learning (RL) enables policy optimization from static datasets but is inherently vulnerable to data poisoning attacks. Existing attack strategies typically re…