no code implementations • 27 Jun 2022 • Chenhan Jin, Kaiwen Zhou, Bo Han, Ming-Chang Yang, James Cheng
In this paper, we resolve this issue and derive the first high-probability bounds for the private stochastic method with clipping.
1 code implementation • Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2019 • Yifan Hou, Hongzhi Chen, Changji Li, James Cheng, Ming-Chang Yang
Representation learning on graphs, also called graph embedding, has demonstrated its significant impact on a series of machine learning applications such as classification, prediction and recommendation.
1 code implementation • ICLR 2020 • Yifan Hou, Jian Zhang, James Cheng, Kaili Ma, Richard T. B. Ma, Hongzhi Chen, Ming-Chang Yang
Graph neural networks (GNNs) have been widely used for representation learning on graph data.
no code implementations • 30 Sep 2019 • Jie Liu, Xiao Yan, Xinyan Dai, Zhirong Li, James Cheng, Ming-Chang Yang
Then we explain the good performance of ip-NSW as matching the norm bias of the MIPS problem - large norm items have big in-degrees in the ip-NSW proximity graph and a walk on the graph spends the majority of computation on these items, thus effectively avoids unnecessary computation on small norm items.