Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Network
This work addresses the problem of scalable and accurate modeling for multi-output data with missing values, particularly in domains like neuroscience, though it is incremental as it builds on existing variational and Gaussian process methods.
The paper tackles efficient inference for structured Gaussian process regression networks by introducing a variational framework with auxiliary inducing variables, enabling scalable modeling of outputs with non-shared inputs and missing data, and demonstrates improved imputation results over state-of-the-art methods on synthetic and real datasets.
This paper presents an efficient variational inference framework for deriving a family of structured gaussian process regression network (SGPRN) models. The key idea is to incorporate auxiliary inducing variables in latent functions and jointly treats both the distributions of the inducing variables and hyper-parameters as variational parameters. Then we propose structured variable distributions and marginalize latent variables, which enables the decomposability of a tractable variational lower bound and leads to stochastic optimization. Our inference approach is able to model data in which outputs do not share a common input set with a computational complexity independent of the size of the inputs and outputs and thus easily handle datasets with missing values. We illustrate the performance of our method on synthetic data and real datasets and show that our model generally provides better imputation results on missing data than the state-of-the-art. We also provide a visualization approach for time-varying correlation across outputs in electrocoticography data and those estimates provide insight to understand the neural population dynamics.