19 citations · 27 across the 4 of their papers we have counts for
4 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…
Robust One Round Federated Learning with Predictive Space Bayesian Inference
Mohsin Hasan, Zehao Zhang, Kaiyang Guo +4
Making predictions robust is an important challenge. A separate challenge in federated learning (FL) is to reduce the number of communication rounds, particularly since doing so re…
Personalized Federated Learning via Variational Bayesian Inference
Xu Zhang, Yinchuan Li, Wenpeng Li +2
Federated learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients. To address these challenges, this paper proposes a…
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process
Haolin Yu, Kaiyang Guo, Mahdi Karami +3
In typical scenarios where the Federated Learning (FL) framework applies, it is common for clients to have insufficient training data to produce an accurate model. Thus, models tha…