Approximation Methods for Gaussian Process Regression

By Joaquin Quiñonero-Candela, Carl E. Rasmussen, Cki Williams, Bottou, Olivier Chapelle, Dennis DeCoste, Jason Aaron Edward Weston. 2007

202 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Andrew Gordon Wilson
2. Jianfei Cai
3. David S. Bindel
4. Yew-Soon Ong
5. Haitao Liu
6. Hannes Nickisch
7. David Eriksson
8. Kun Dong

In Computational Theory and Mathematics

1. Andrew Gordon Wilson
2. Jianfei Cai
3. Haitao Liu
4. Yew-Soon Ong
5. Melanie Nicole Zeilinger
6. Lukas Hewing
7. Hannes Nickisch
8. David S. Bindel

In Control and Systems Engineering

1. Lukas Hewing
2. Melanie Nicole Zeilinger
3. Andrew Gordon Wilson
4. Edwin V. Bonilla
5. Yew-Soon Ong
6. Jianfei Cai
7. Haitao Liu
8. Juraj Kabzan

Citations in the corpus

• Financial modeling using Gaussian process models (2011, methodology): “To overcome the computational-limitation issues and to also make use of the method for large-scale dataset applications, numerous authors have suggested various sparse approximations [19], [20].”
• Scalable Log Determinants for Gaussian Process Kernel Learning (2017, background): “This modification has had exceptional practical significance, leading to improved point predictions and much more realistic predictive uncertainty [8, 28], making FITC arguably the most popular approach for scalable Gaussian processes.”
• Remarks on multi-output Gaussian process regression (2018, methodology): “Finally, to reduce the computational complexity of calculating K−1 M for cases with large-scale training data, some sparse approximations that use m (m N) inducing points to approximate the large covariance matrix, or use the variational inference to approximate the lower bound of p(y), have been ap”
• Cautious Model Predictive Control Using Gaussian Process Regression (2017, methodology): “To overcome these issues, various approximation techniques have been proposed, one class of which is sparse Gaussian processes using inducing inputs [20], briefly outlined in the following.”
• Multi-task Gaussian Process Prediction (2007, methodology): “The issue of dealing with large N has been much studied in the GP literature, see [10, ch. 8] and [ 11 ] for overviews.”
• Algorithm runtime prediction: Methods & evaluation (2012, unknown): “, [93]).”
• Empowerment for continuous agent—environment systems (2011, methodology): “For brevity we will only sketch our particular implementation, see Quiñonero-Candela, Rasmussen, and Williams (2007) for more detailed information.”