Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression

Understand

In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts.

• The state-of-the-art aggregation models, however, either provide inconsistent predictions or require time-consuming aggregation process.
• We first prove the inconsistency of typical aggregations using disjoint or random data partition, and then present a consistent yet efficient aggregation model for large-scale GP.
• The proposed model inherits the advantages of aggregations, e.g., closed-form inference and aggregation, parallelization and distributed computing.