Statistics > Machine Learning
[Submitted on 25 Nov 2013 (v1), last revised 27 Nov 2013 (this version, v3)]
Title:On Approximate Inference for Generalized Gaussian Process Models
View PDFAbstract:A generalized Gaussian process model (GGPM) is a unifying framework that encompasses many existing Gaussian process (GP) models, such as GP regression, classification, and counting. In the GGPM framework, the observation likelihood of the GP model is itself parameterized using the exponential family distribution (EFD). In this paper, we consider efficient algorithms for approximate inference on GGPMs using the general form of the EFD. A particular GP model and its associated inference algorithms can then be formed by changing the parameters of the EFD, thus greatly simplifying its creation for task-specific output domains. We demonstrate the efficacy of this framework by creating several new GP models for regressing to non-negative reals and to real intervals. We also consider a closed-form Taylor approximation for efficient inference on GGPMs, and elaborate on its connections with other model-specific heuristic closed-form approximations. Finally, we present a comprehensive set of experiments to compare approximate inference algorithms on a wide variety of GGPMs.
Submission history
From: Antoni Chan [view email][v1] Mon, 25 Nov 2013 17:22:22 UTC (5,817 KB)
[v2] Tue, 26 Nov 2013 04:24:02 UTC (2,597 KB)
[v3] Wed, 27 Nov 2013 07:43:48 UTC (2,595 KB)
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