Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression
This addresses the computational bottleneck in Gaussian process models for researchers and practitioners dealing with massive datasets, though it appears incremental as it builds on existing mixture-of-experts and hierarchical approaches.
The authors tackled the problem of scaling Gaussian process regression to large datasets by proposing a hierarchical mixture-of-experts model that enables efficient parallelization and handles millions of data points without sparse approximations, achieving practical scalability for nonlinear probabilistic regression.
We propose a practical and scalable Gaussian process model for large-scale nonlinear probabilistic regression. Our mixture-of-experts model is conceptually simple and hierarchically recombines computations for an overall approximation of a full Gaussian process. Closed-form and distributed computations allow for efficient and massive parallelisation while keeping the memory consumption small. Given sufficient computing resources, our model can handle arbitrarily large data sets, without explicit sparse approximations. We provide strong experimental evidence that our model can be applied to large data sets of sizes far beyond millions. Hence, our model has the potential to lay the foundation for general large-scale Gaussian process research.