82 citations · 87 across the 6 of their papers we have counts for
6 papers
Federated Nested Learning: Collaborative Training of Self-Referential Memories for Test-Time Adaptation
Hong Chen, Pengcheng Wu, Yuanguo Lin +4
We rethink Federated Learning (FL) from a nested learning perspective, framing the core challenge as how to collaboratively learn optimization rules, not just static models, to tac…
Sharpness-aware Federated Graph Learning
Ruiyu Li, Peige Zhao, Guangxia Li +3
One of many impediments to applying graph neural networks (GNNs) to large-scale real-world graph data is the challenge of centralized training, which requires aggregating data from…
A Survey on Privacy Risks and Protection in Large Language Models
Kang Chen, Xiuze Zhou, Yuanguo Lin +3
Although Large Language Models (LLMs) have become increasingly integral to diverse applications, their capabilities raise significant privacy concerns. This survey offers a compreh…
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
Yuanguo Lin, Fan Lin, Guorong Cai +3
In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a…
Multi-scale Attention Flow for Probabilistic Time Series Forecasting
Shibo Feng, Chunyan Miao, Ke Xu +4
The probability prediction of multivariate time series is a notoriously challenging but practical task. On the one hand, the challenge is how to effectively capture the cross-serie…
A Survey on Reinforcement Learning for Recommender Systems
Yuanguo Lin, Yong Liu, Fan Lin +5
Recommender systems have been widely applied in different real-life scenarios to help us find useful information. In particular, Reinforcement Learning (RL) based recommender syste…