116 citations · 120 across the 6 of their papers we have counts for
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cs.LG2024
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer
Xin Gao, Xin Yang, Hao Yu +2
Federated Class-Incremental Learning (FCIL) focuses on continually transferring the previous knowledge to learn new classes in dynamic Federated Learning (FL). However, existing me…
cs.LG2024★ 1 cited
Open Continual Feature Selection via Granular-Ball Knowledge Transfer
Xuemei Cao, Xin Yang, Shuyin Xia +2
This paper presents a novel framework for continual feature selection (CFS) in data preprocessing, particularly in the context of an open and dynamic environment where unknown clas…
cs.LG2024
Multi-granularity Knowledge Transfer for Continual Reinforcement Learning
Chaofan Pan, Lingfei Ren, Yihui Feng +4
Continual reinforcement learning (CRL) empowers RL agents with the ability to learn a sequence of tasks, accumulating knowledge learned in the past and using the knowledge for prob…