Search Machine Learning Repository:
Fast Allocation of Gaussian Process Experts
Authors: Trung Nguyen and Edwin Bonilla
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
Abstract: We propose a scalable nonparametric Bayesian regression model based on a mixture of Gaussian process (GP) experts and the inducing points formalism underpinning sparse GP approximations. Each expert is augmented with a set of inducing points, and the allocation of data points to experts is defined probabilistically based on their proximity to the experts. This allocation mechanism enables a fast variational inference procedure for learning of the inducing inputs and hyperparameters of the experts. When using $K$ experts, our method can run $K^2$ times faster and use $K^2$ times less memory than popular sparse methods such as the FITC approximation. Furthermore, it is easy to parallelize and handles non-stationarity straightforwardly. Our experiments show that on medium-sized datasets (of around $10^4$ training points) it trains up to 5 times faster than FITC while achieving comparable accuracy. On a large dataset of $10^5$ training points, our method significantly outperforms six competitive baselines while requiring only a few hours of training.
authors venues years
Suggest Changes to this paper.
Brought to you by the WUSTL Machine Learning Group. We have open faculty positions (tenured and tenure-track).