# Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes

@article{Wild2021ConnectionsAE, title={Connections and Equivalences between the Nystr{\"o}m Method and Sparse Variational Gaussian Processes}, author={Veit Wild and Motonobu Kanagawa and D. Sejdinovic}, journal={ArXiv}, year={2021}, volume={abs/2106.01121} }

We investigate the connections between sparse approximation methods for making kernel methods and Gaussian processes (GPs) scalable to massive data, focusing on the Nyström method and the Sparse Variational Gaussian Processes (SVGP). While sparse approximation methods for GPs and kernel methods share some algebraic similarities, the literature lacks a deep understanding of how and why they are related. This is a possible obstacle for the communications between the GP and kernel communities…

## 4 Citations

Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning

- Computer ScienceICML
- 2022

Novel confidence intervals are provided for the Nyström method and the sparse variational Gaussian process approximation method, which are established using novel interpretations of the approximate (surrogate) posterior variance of the models.

Posterior and Computational Uncertainty in Gaussian Processes

- Computer ScienceArXiv
- 2022

A new class of methods is developed that provides consistent estimation of the combined uncertainty arising from both the finite number of data observed and the finite amount of computation expended, and the consequences of ignoring computational uncertainty are demonstrated.

Variational Gaussian Processes: A Functional Analysis View

- Computer ScienceAISTATS
- 2022

This work proposes to view the GP as lying in a Banach space which then facilitates a uniﬁed perspective and is used to understand the relationship between existing features and to draw a connection between kernel ridge regression and variational GP approximations.

Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domain by Adaptive Discretization

- Computer ScienceAISTATS
- 2022

Ada-BKB (Adaptive Budgeted Kernelized Bandit), a no-regret Gaussian process optimization algorithm for functions on continuous domains, that provably runs in O, where d eﬀ is the e-ective dimension of the explored space, and which is typically much smaller than T .

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