2013/05/24 by Jie Chen, Chen, Jie, Nannan Cao +10 · 1 citation
Computer Science · Engineering · Mathematics · #Advanced Multi-Objective Optimization Algorithms #Control Systems and Identification #Distributed #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Parallel #and Cluster Computing (cs.DC) #cs.DC #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1305.5826
29th Conference on Uncertainty in Artificial Intelligence (UAI 2013), Extended version with proofs, 13 pages
arxiv created 2013/05/24 · openalex publication_date 2013/05/24 · arxiv updated 2013/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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-time predictions due to its cubic time cost in the data size. This paper presents two parallel GP regression methods that exploit low-rank covariance matrix approximations for distributing the computational load among parallel machines to achieve time efficiency and scalability. We theoretically guarantee the predictive performances of our proposed parallel GPs to be equivalent to that of some centralized approximate GP regression methods: The computation of their centralized counterparts can be distributed among parallel machines, hence achieving greater time efficiency and scalability. We analytically compare the properties of our parallel GPs such as time, space, and communication complexity. Empirical evaluation on two real-world datasets in a cluster of 20 computing nodes shows that our parallel GPs are significantly more time-efficient and scalable than their centralized counterparts and exact/full GP while achieving predictive performances comparable to full GP.