98 citations · 196 across the 32 of their papers we have counts for
Showing 2018 · stat.MLShow all
2 papers · 2 filters
stat.ML2018★ 2 cited
Fully Scalable Gaussian Processes using Subspace Inducing Inputs
Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias
We introduce fully scalable Gaussian processes, an implementation scheme that tackles the problem of treating a high number of training instances together with high dimensional inp…
stat.ML2018★ 5 cited
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers
Marcel Hirt, Petros Dellaportas
We present a scalable approach to performing approximate fully Bayesian inference in generic state space models. The proposed method is an alternative to particle MCMC that provide…