Asynchronous Distributed Variational Gaussian Process for Regression

@inproceedings{Peng2017AsynchronousDV,
  title={Asynchronous Distributed Variational Gaussian Process for Regression},
  author={Hao Peng and Shandian Zhe and Xiao Zhang and Yuan Qi},
  booktitle={ICML},
  year={2017}
}
Gaussian processes (GPs) are powerful nonparametric function estimators. However, their applications are largely limited by the expensive computational cost of the inference procedures. Existing stochastic or distributed synchronous variational inferences, although have alleviated this issue by scaling up GPs to millions of samples, are still far from satisfactory for real-world large applications, where the data sizes are often orders of magnitudes larger, say, billions. To solve this problem… CONTINUE READING