24 citations · 77 across the 23 of their papers we have counts for
4 papers · 1 filter
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data
Xinwei Zhang, Mingyi Hong, Jie Chen
Vertical federated learning (VFL) is a distributed learning paradigm, where computing clients collectively train a model based on the partial features of the same set of samples th…
Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning
Zehao Niu, Mihai Anitescu, Jie Chen
Gaussian processes (GPs) are an attractive class of machine learning models because of their simplicity and flexibility as building blocks of more complex Bayesian models. Meanwhil…
A Sequential Set Generation Method for Predicting Set-Valued Outputs
Tian Gao, Jie Chen, Vijil Chenthamarakshan +1
Consider a general machine learning setting where the output is a set of labels or sequences. This output set is unordered and its size varies with the input. Whereas multi-label c…
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Jie Chen, Nannan Cao, Kian Hsiang Low +3
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-t…