icml2014c2_bratieres14.pdf (847.58 kB)
Scalable Gaussian process structured prediction for grid factor graph applications
conference contribution
posted on 2023-06-08, 21:11 authored by Sebastien Bratieres, Novi QuadriantoNovi Quadrianto, Sebastian Nowozin, Zoubin GhahramaniStructured prediction is an important and well studied problem with many applications across machine learning. GPstruct is a recently proposed structured prediction model that offers appealing properties such as being kernelised, non-parametric, and supporting Bayesian inference (Bratières et al. 2013). The model places a Gaussian process prior over energy functions which describe relationships between input variables and structured output variables. However, the memory demand of GPstruct is quadratic in the number of latent variables and training runtime scales cubically. This prevents GPstruct from being applied to problems involving grid factor graphs, which are prevalent in computer vision and spatial statistics applications. Here we explore a scalable approach to learning GPstruct models based on ensemble learning, with weak learners (predictors) trained on subsets of the latent variables and bootstrap data, which can easily be distributed. We show experiments with 4M latent variables on image segmentation. Our method outperforms widely-used conditional random field models trained with pseudo-likelihood. Moreover, in image segmentation problems it improves over recent state-of-the-art marginal optimisation methods in terms of predictive performance and uncertainty calibration. Finally, it generalises well on all training set sizes.
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Publication status
- Published
File Version
- Published version
Journal
Proceedings of the 32nd International Conference on Machine Learning; Beijing, China; 21 - 26 June 2014ISSN
1938-7228Publisher
JMLRPublisher URL
Issue
2Volume
32Page range
334-342Event name
International Conference on Machine Learning (ICML)Event location
BeijingEvent type
conferenceDepartment affiliated with
- Informatics Publications
Notes
Proceedings of the 31 st International Conference on Machine Learning, Beijing, China, 2014Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2015-06-18First Open Access (FOA) Date
2015-06-18First Compliant Deposit (FCD) Date
2015-06-18Usage metrics
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