Actually Sparse Variational Gaussian Processes
Gaussian processes (GPs) are typically criticised for their unfavourable scaling in both computational and memory requirements. For large datasets, sparse GPs reduce these demands by conditioning on a small set of inducing variables designed to summarise the data. In practice however, for large datasets requiring many inducing variables, such as low-lengthscale spatial data, even sparse GPs can become computationally expensive, limited by the number of inducing variables one can use. In this work, we propose a new class of inter-domain variational GP, constructed by projecting a GP onto a set of compactly supported B-spline basis functions. The key benefit of our approach is that the compact support of the B-spline basis functions admits the use of sparse linear algebra to significantly speed up matrix operations and drastically reduce the memory footprint. This allows us to very efficiently model fast-varying spatial phenomena with tens of thousands of inducing variables, where previous approaches failed.
Code (1)
Tasks
Gaussian ProcessesMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Sparse Orthogonal Variational Inference for Gaussian Processes
We introduce a new interpretation of sparse variational approximations for Gaussian processes using inducing points, which can lead to more scalable algorithms than previous methods. It is based on decomposing a Gaussian…
Gaussian ProcessesMulti-class ClassificationregressionVariational InferenceVariational Elliptical Processes
We present elliptical processes, a family of non-parametric probabilistic models that subsume Gaussian processes and Student's t processes. This generalization includes a range of new heavy-tailed behaviors while retaini…
Gaussian ProcessesVariational InferenceDecoupled Sparse Gaussian Processes Components]{Decoupled Sparse Gaussian Processes Components : Separating Decision Making from Data Manifold Fitting
We propose a decoupling in Reproducing Kernel Hilbert Space of the parametric and non-parametric components of Sparse Gaussian Processes. We demonstrate that this decoupling results in a set of inducing points concentra…
Decision MakingGaussian ProcessesregressionScalable GAM using sparse variational Gaussian processes
Generalized additive models (GAMs) are a widely used class of models of interest to statisticians as they provide a flexible way to design interpretable models of data beyond linear models. We here propose a scalable and…
Additive modelsGaussian ProcessesVariational InferenceVariational zero-inflated Gaussian processes with sparse kernels
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 lead…
Gaussian ProcessesVariational Inference