57 citations · 165 across the 7 of their papers we have counts for
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stat.ML2014
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…
stat.ML2013★ 36 cited
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…