Multi-task Learning in Deep Gaussian Processes with Multi-kernel Layers.
Year: 2019
Type: preprint
Abstract: We present a multi-task learning formulation for Deep Gaussian processes (DGPs), describing a multi-kernel architecture for DGP layers. The proposed model is a non-linear mixture of latent Gaussian processes (GPs) with components shared between the tasks, in addition to separate task-specific components. Our formulation allows for learning complex relationships between tasks. We benchmark our model on three real-world datasets showing empirically that our formulation is able to improve the learning performance and transfer information between the tasks, outperforming state-of-the-art GP-based single-task learning and multi-task learning models.
Source: arXiv (Cornell University)
Authors: Ayman Boustati, Richard S. Savage
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2947848606