12 citations · 29 across the 7 of their papers we have counts for
9 papers
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief
Kaiyang Guo, Yunfeng Shao, Yanhui Geng
Model-based offline reinforcement learning (RL) aims to find highly rewarding policy, by leveraging a previously collected static dataset and a dynamics model. While the dynamics m…
Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again
Xin-Chun Li, Wen-Shu Fan, Shaoming Song +4
Knowledge Distillation (KD) aims at transferring the knowledge of a well-performed neural network (the {\it teacher}) to a weaker one (the {\it student}). A peculiar phenomenon is…
On the Convergence Theory of Meta Reinforcement Learning with Personalized Policies
Haozhi Wang, Qing Wang, Yunfeng Shao +3
Modern meta-reinforcement learning (Meta-RL) methods are mainly developed based on model-agnostic meta-learning, which performs policy gradient steps across tasks to maximize polic…
Federated Learning with Position-Aware Neurons
Xin-Chun Li, Yi-Chu Xu, Shaoming Song +4
Federated Learning (FL) fuses collaborative models from local nodes without centralizing users' data. The permutation invariance property of neural networks and the non-i.i.d. data…
Domain Adaptation without Model Transferring
Kunhong Wu, Yucheng Shi, Yahong Han +3
In recent years, researchers have been paying increasing attention to the threats brought by deep learning models to data security and privacy, especially in the field of domain ad…
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes
Xin-Chun Li, Le Gan, De-Chuan Zhan +3
Although federated learning (FL) has recently been proposed for efficient distributed training and data privacy protection, it still encounters many obstacles. One of these is the…