no code implementations • 17 May 2024 • Song Wang, Yushun Dong, Binchi Zhang, Zihan Chen, Xingbo Fu, Yinhan He, Cong Shen, Chuxu Zhang, Nitesh V. Chawla, Jundong Li
In this survey paper, we explore three critical aspects vital for enhancing safety in Graph ML: reliability, generalizability, and confidentiality.
1 code implementation • 17 Jun 2023 • Song Wang, Xingbo Fu, Kaize Ding, Chen Chen, Huiyuan Chen, Jundong Li
In this way, the server can exploit the computational power of all clients and train the model on a larger set of data samples among all clients.
no code implementations • 2 May 2023 • Xingbo Fu, Chen Chen, Yushun Dong, Anil Vullikanti, Eili Klein, Gregory Madden, Jundong Li
In this paper, we propose a novel problem of antibiogram pattern prediction that aims to predict which patterns will appear in the future.
no code implementations • 24 Jul 2022 • Xingbo Fu, Binchi Zhang, Yushun Dong, Chen Chen, Jundong Li
Federated Graph Machine Learning (FGML) is a promising solution to tackle this challenge by training graph machine learning models in a federated manner.
no code implementations • 26 Feb 2020 • Xingbo Fu, Feng Gao, Jiang Wu
In this paper, we propose an actor-critic method - Attention-based Twin Delayed Deep Deterministic policy gradient (ATD3) algorithm to approximate a driver' s action according to observations and measure the driver' s attention allocation for consecutive time steps in car-following model.
1 code implementation • 14 Sep 2019 • Xingbo Fu, Feng Gao, Jiang Wu, Xinyu Wei, Fangwei Duan
This model captures spatial correlations among wind farms and temporal dependencies of wind power time series.