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cs.LG2025
Little by Little: Continual Learning via Incremental Mixture of Rank-1 Associative Memory Experts
Haodong Lu, Chongyang Zhao, Minhui Xue +3
Continual learning (CL) with large pre-trained models aims to incrementally acquire knowledge without catastrophic forgetting. Existing LoRA-based Mixture-of-Experts (MoE) methods…
cs.LG2025
CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius Maximization
Derui Wang, Kristen Moore, Diksha Goel +8
Deep reinforcement learning (DRL) has gained widespread adoption in control and decision-making tasks due to its strong performance in dynamic environments. However, DRL agents are…
cs.LG2024
Provably Unlearnable Data Examples
Derui Wang, Minhui Xue, Bo Li +2
The exploitation of publicly accessible data has led to escalating concerns regarding data privacy and intellectual property (IP) breaches in the age of artificial intelligence. To…