Zero-inflated datasets, which have an excess of zero outputs, are commonly encountered in problems such as climate or rare event modelling. Conventional machine learning approaches tend to overestimate the non-zeros leading to poor performance. We propose a novel model family of zero-inflated Gaussian processes (ZiGP) for such zero-inflated datasets, produced by sparse kernels through learning a latent probit Gaussian process that can zero out kernel rows and columns whenever the signal is absent. The ZiGPs are particularly useful for making the powerful Gaussian process networks more interpretable. We introduce sparse GP networks where variable-order latent modelling is achieved through sparse mixing signals. We derive the non-trivial stochastic variational inference tractably for scalable learning of the sparse kernels in both models. The novel output-sparse approach improves both prediction of zero-inflated data and inter-pretability of latent mixing models.
|Title of host publication||34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018|
|Publication status||Published - 2018|
|MoE publication type||A4 Article in a conference publication|
|Event||Conference on Uncertainty in Artificial Intelligence - Monterey, United States|
Duration: 6 Aug 2018 → 10 Aug 2018
|Conference||Conference on Uncertainty in Artificial Intelligence|
|Period||06/08/2018 → 10/08/2018|