论文信息 - Transductive Log Opinion Pool of Gaussian Process Experts

Transductive Log Opinion Pool of Gaussian Process Experts

We introduce a framework for analyzing transductive combination of Gaussian process (GP) experts, where independently trained GP experts are combined in a way that depends on test point location, in order to scale GPs to big data. The framework provides some theoretical justification for the generalized product of GP experts (gPoE-GP) which was previously shown to work well in practice but lacks theoretical basis. Based on the proposed framework, an improvement over gPoE-GP is introduced and empirically validated.

David J. Fleet | Yanshuai Cao

[1] David J. Fleet,et al. Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions , 2014, ArXiv.

[2] Stephen M. Omohundro,et al. Five Balltree Construction Algorithms , 2009 .

[3] Marc Peter Deisenroth,et al. Distributed Gaussian Processes , 2015, ICML.

[4] Tom Heskes,et al. Selecting Weighting Factors in Logarithmic Opinion Pools , 1997, NIPS.

[5] Michalis K. Titsias,et al. Variational Learning of Inducing Variables in Sparse Gaussian Processes , 2009, AISTATS.

[6] David J. Fleet,et al. Efficient Optimization for Sparse Gaussian Process Regression , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7] Zoubin Ghahramani,et al. Sparse Gaussian Processes using Pseudo-inputs , 2005, NIPS.

[8] Volker Tresp,et al. A Bayesian Committee Machine , 2000, Neural Computation.

[9] Trevor Darrell,et al. Sparse probabilistic regression for activity-independent human pose inference , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.