Abstract
Gaussian processes (GPs) are non-parametric, flexible, models that work well in many tasks. Combining GPs with deep learning methods via deep kernel learning (DKL) is especially compelling due to the strong representational power induced by the network. However, inference in GPs, whether with or without DKL, can be computationally challenging on large datasets. Here, we propose GP-Tree, a novel method for multi-class classification with Gaussian processes and DKL. We develop a tree-based hierarchical model in which each internal node of the tree fits a GP to the data using the Pòlya-Gamma augmentation scheme. As a result, our method scales well with both the number of classes and data size. We demonstrate the effectiveness of our method against other Gaussian process training baselines, and we show how our general GP approach achieves improved accuracy on standard incremental few-shot learning benchmarks.
Original language | English |
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Title of host publication | Proceedings of the 38th International Conference on Machine Learning, ICML 2021 |
Publisher | ML Research Press |
Pages | 54-65 |
Number of pages | 12 |
ISBN (Electronic) | 9781713845065 |
State | Published - 2021 |
Event | 38th International Conference on Machine Learning, ICML 2021 - Virtual, Online Duration: 18 Jul 2021 → 24 Jul 2021 |
Publication series
Name | Proceedings of Machine Learning Research |
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Volume | 139 |
ISSN (Electronic) | 2640-3498 |
Conference
Conference | 38th International Conference on Machine Learning, ICML 2021 |
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City | Virtual, Online |
Period | 18/07/21 → 24/07/21 |
Bibliographical note
Publisher Copyright:Copyright © 2021 by the author(s)
Funding
This study was funded by a grant to GC from the Israel Science Foundation (ISF 737/2018), and by an equipment grant to GC and Bar-Ilan University from the Israel Science Foundation (ISF 2332/18). IA was funded by a grant from the Israeli innovation authority, through the AVATAR consortium.
Funders | Funder number |
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Israel Science Foundation | ISF 2332/18, ISF 737/2018 |
Israel Innovation Authority |