@inproceedings{0e1d7aa6d6ff475d84a15b46c5fd2670,
title = "Sparse within Sparse Gaussian Processes using Neighbor Information",
abstract = "Approximations to Gaussian processes (GPs) based on inducing variables, combined with variational inference techniques, enable state-of-the-art sparse approaches to infer GPs at scale through mini-batch based learning. In this work, we further push the limits of scalability of sparse GPs by allowing large number of inducing variables without imposing a special structure on the inducing inputs. In particular, we introduce a novel hierarchical prior, which imposes sparsity on the set of inducing variables. We treat our model variationally, and we experimentally show considerable computational gains compared to standard sparse GPs when sparsity on the inducing variables is realized considering the nearest inducing inputs of a random mini-batch of the data. We perform an extensive experimental validation that demonstrates the effectiveness of our approach compared to the state-of-the-art. Our approach enables the possibility to use sparse GPs using a large number of inducing points without incurring a prohibitive computational cost.",
author = "Tran, {Gia Lac} and Dimitrios Milios and Pietro Michiardi and Maurizio Filippone",
note = "Publisher Copyright: Copyright {\textcopyright} 2021 by the author(s); 38th International Conference on Machine Learning, ICML 2021 ; Conference date: 18-07-2021 Through 24-07-2021",
year = "2021",
language = "English (US)",
series = "Proceedings of Machine Learning Research",
publisher = "Mathematical Research Press",
pages = "10369--10378",
booktitle = "Proceedings of the 38th International Conference on Machine Learning, ICML 2021",
address = "United Kingdom",
}