4 citations · 7 across the 6 of their papers we have counts for
6 papers
A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility
Kejie Chen, Yanqing Li, Rongxin Zhou +1
The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. To forecast the t…
Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains
Haitao Liu, Kai Wu, Yew-Soon Ong +3
Multi-task Gaussian process (MTGP) is a well-known non-parametric Bayesian model for learning correlated tasks effectively by transferring knowledge across tasks. But current MTGPs…
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization
Haitao Liu, Jiaqi Ding, Xinyu Xie +3
Multi-task regression attempts to exploit the task similarity in order to achieve knowledge transfer across related tasks for performance improvement. The application of Gaussian p…
Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems
Haitao Liu, Changjun Liu, Xiaomo Jiang +3
The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios, for example, system identification and prognostic and health manag…
Modulating Scalable Gaussian Processes for Expressive Statistical Learning
Haitao Liu, Yew-Soon Ong, Xiaomo Jiang +1
For a learning task, Gaussian process (GP) is interested in learning the statistical relationship between inputs and outputs, since it offers not only the prediction mean but also…
Deep Latent-Variable Kernel Learning
Haitao Liu, Yew-Soon Ong, Xiaomo Jiang +1
Deep kernel learning (DKL) leverages the connection between Gaussian process (GP) and neural networks (NN) to build an end-to-end, hybrid model. It combines the capability of NN to…