Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression

• URL: http://arxiv.org/abs/2107.08265v1
• Date: Sat, 17 Jul 2021 15:55:35 GMT
• Title: Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression
• Authors: Ayush Jain (1), P. K. Srijith (1) and Mohammad Emtiyaz Khan (2) ((1) Department of Computer Science and Engineering, Indian Institute of Technology Hyderabad, India, (2) RIKEN Center for AI Project, Tokyo, Japan)
• Abstract summary: Deep Gaussian Processes (DGPs) are multi-layer, flexible extensions of Gaussian processes. Sparse approximations simplify the training but often require optimization over a large number of inducing inputs and their locations. In this paper, we simplify the training by setting the locations to a fixed subset of data and sampling the inducing inputs from a variational distribution.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Gaussian Processes (DGPs) are multi-layer, flexible extensions of Gaussian processes but their training remains challenging. Sparse approximations simplify the training but often require optimization over a large number of inducing inputs and their locations across layers. In this paper, we simplify the training by setting the locations to a fixed subset of data and sampling the inducing inputs from a variational distribution. This reduces the trainable parameters and computation cost without significant performance degradations, as demonstrated by our empirical results on regression problems. Our modifications simplify and stabilize DGP training while making it amenable to sampling schemes for setting the inducing inputs.

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