On Approximate Inference for Generalized Gaussian Process Models
This work provides a unifying framework for creating task-specific Gaussian process models, which is incremental as it builds on existing GP methods.
The paper tackles efficient approximate inference for generalized Gaussian process models (GGPMs) that unify various GP models via exponential family distributions, resulting in new GP models for non-negative and interval regression with demonstrated efficacy through comprehensive experiments.
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.