Generalized Gaussian process models

We propose a generalized Gaussian process model (GGPM), which 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. By deriving approximate inference algorithms for the generalized GP model, we are able to easily apply the same algorithm to all other GP models. Novel GP models are created by changing the parameterization of the likelihood function, which greatly simplifies their creation for task-specific output domains. We also derive a closed-form efficient Taylor approximation for inference on the model, and draw interesting connections with other model-specific closed-form approximations. Finally, using the GGPM, we create several new GP models and show their efficacy in building task-specific GP models for computer vision.