57 citations · 165 across the 7 of their papers we have counts for
8 papers
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression
Tong Teng, Jie Chen, Yehong Zhang +1
This paper presents a variational Bayesian kernel selection (VBKS) algorithm for sparse Gaussian process regression (SGPR) models. In contrast to existing GP kernel selection algor…
Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation
Kian Hsiang Low, Jiangbo Yu, Jie Chen +1
The expressive power of a Gaussian process (GP) model comes at a cost of poor scalability in the data size. To improve its scalability, this paper presents a low-rank-cum-Markov ap…
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…
Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena
Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan +4
The problem of modeling and predicting spatiotemporal traffic phenomena over an urban road network is important to many traffic applications such as detecting and forecasting conge…
GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model
Nuo Xu, Kian Hsiang Low, Jie Chen +2
Central to robot exploration and mapping is the task of persistent localization in environmental fields characterized by spatially correlated measurements. This paper presents a Ga…
Gaussian Process-Based Decentralized Data Fusion and Active Sensing for Mobility-on-Demand System
Jie Chen, Kian Hsiang Low, Colin Keng-Yan Tan
Mobility-on-demand (MoD) systems have recently emerged as a promising paradigm of one-way vehicle sharing for sustainable personal urban mobility in densely populated cities. In th…