214 citations · 487 across the 24 of their papers we have counts for
4 papers · 1 filter
Recurrent Gaussian Processes
César Lincoln C. Mattos, Zhenwen Dai, Andreas Damianou +3
We define Recurrent Gaussian Processes (RGP) models, a general family of Bayesian nonparametric models with recurrent GP priors which are able to learn dynamical patterns from sequ…
Variational Auto-encoded Deep Gaussian Processes
Zhenwen Dai, Andreas Damianou, Javier González +1
We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model. Inference is performed in a novel scalable variational fr…
Spike and Slab Gaussian Process Latent Variable Models
Zhenwen Dai, James Hensman, Neil Lawrence
The Gaussian process latent variable model (GP-LVM) is a popular approach to non-linear probabilistic dimensionality reduction. One design choice for the model is the number of lat…
Batch Bayesian Optimization via Local Penalization
Javier González, Zhenwen Dai, Philipp Hennig +1
The popularity of Bayesian optimization methods for efficient exploration of parameter spaces has lead to a series of papers applying Gaussian processes as surrogates in the optimi…