Timezone: »

Beyond Intuition, a Framework for Applying GPs to Real-World Data
Kenza Tazi · Jihao Andreas Lin · ST John · Hong Ge · Richard E Turner · Ross Viljoen · Alex Gardner
Event URL: https://openreview.net/forum?id=d6phEU6UaG »

Gaussian Processes (GPs) offer an attractive method for regression over small, structured and correlated datasets. However, their deployment is hindered by computational costs and limited guidelines on how to apply GPs beyond simple low-dimensional datasets. We propose a framework to identify the suitability of GPs to a given problem and how to set up a robust and well-specified GP model. The guidelines formalise the decisions of experienced GP practitioners, with an emphasis on kernel design and scaling options. The framework is then applied to a case study of glacier elevation change and yields more accurate results at test time.

Author Information

Kenza Tazi (University of Cambridge)
Jihao Andreas Lin (University of Cambridge)
ST John (Aalto University, Finnish Center for Artificial Intelligence)
Hong Ge (University of Cambridge)
Richard E Turner (University of Cambridge)

Richard Turner holds a Lectureship (equivalent to US Assistant Professor) in Computer Vision and Machine Learning in the Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, UK. He is a Fellow of Christ's College Cambridge. Previously, he held an EPSRC Postdoctoral research fellowship which he spent at both the University of Cambridge and the Laboratory for Computational Vision, NYU, USA. He has a PhD degree in Computational Neuroscience and Machine Learning from the Gatsby Computational Neuroscience Unit, UCL, UK and a M.Sci. degree in Natural Sciences (specialism Physics) from the University of Cambridge, UK. His research interests include machine learning, signal processing and developing probabilistic models of perception.

Ross Viljoen (University of Cambridge)
Alex Gardner (Jet Propulsion Laboratory)

More from the Same Authors