Efficient Multioutput Gaussian Processes through Variational Inducing Kernels

By Mauricio A Álvarez, David Luengo, Michalis K. Titsias, Neil D. Lawrence. 2010

142 citations recorded by the source corpus.

How researchers see this paper

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

1. Thang D. Bui
2. Richard E. Turner
3. Ching-An Cheng
4. Sergey Levine
5. Byron Boots
6. Andreas Krause
7. Josiah Yan
8. Yarin Gal

Citations in the corpus

• Contextual Gaussian Process Bandit Optimization (2011, background): “Instead of considering a scalar GP with joint feature space f : S × Z → R, they consider a multioutput GP fvec : S → R , and introduce output correlations as linear combinations of latent channels or convolutions of GPs [25].”
• Incremental Variational Sparse Gaussian Process Regression (2016, methodology): “[3] adopt the same variational approach in the multi-output regression setting with scaled basis functions, and Abdel-Gawad et al.”
• A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation (2016, background): “The VFE approach can also be extended to employ inter-domain variables (Álvarez et al., 2010; Tobar et al., 2015; Matthews et al., 2016).”