Aggregation Models with Optimal Weights for Distributed Gaussian Processes

TL;DR

This paper introduces a new aggregation method for distributed Gaussian processes that efficiently incorporates correlations among experts, improving prediction accuracy and stability while reducing computational time.

Contribution

The paper proposes a novel aggregation approach for distributed GPs that effectively models correlations among experts, enhancing prediction quality and efficiency.

Findings

More stable predictions than existing models

Reduced computational time for aggregation

Improved prediction accuracy with correlated experts

Abstract

Gaussian process (GP) models have received increasing attention in recent years due to their superb prediction accuracy and modeling flexibility. To address the computational burdens of GP models for large-scale datasets, distributed learning for GPs are often adopted. Current aggregation models for distributed GPs is not time-efficient when incorporating correlations between GP experts. In this work, we propose a novel approach for aggregated prediction in distributed GPs. The technique is suitable for both the exact and sparse variational GPs. The proposed method incorporates correlations among experts, leading to better prediction accuracy with manageable computational requirements. As demonstrated by empirical studies, the proposed approach results in more stable predictions in less time than state-of-the-art consistent aggregation models.