Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition

19 October 2017

Papers citing "Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition"

8 / 8 papers shown

Title
Function-Space Regularization for Deep Bayesian Classification J. Lin Joe Watson Pascal Klink Jan Peters UQCV BDL 41 1 0 12 Jul 2023
Kernel Interpolation with Sparse Grids Mohit Yadav Daniel Sheldon Cameron Musco 23 5 0 23 May 2023
Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling Lei Cheng Feng Yin Sergios Theodoridis S. Chatzis Tsung-Hui Chang 68 75 0 28 May 2022
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing Jun Qi Chao-Han Huck Yang Pin-Yu Chen Javier Tejedor 25 16 0 11 Mar 2022
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations Luhuan Wu Andrew C. Miller Lauren Anderson Geoff Pleiss David M. Blei John P. Cunningham 27 8 0 28 Feb 2021
Kernel methods through the roof: handling billions of points efficiently Giacomo Meanti Luigi Carratino Lorenzo Rosasco Alessandro Rudi 24 113 0 18 Jun 2020
Exact Gaussian Processes on a Million Data Points Ke Alexander Wang Geoff Pleiss Jacob R. Gardner Stephen Tyree Kilian Q. Weinberger A. Wilson GP 10 225 0 19 Mar 2019
Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees Jonathan H. Huggins Trevor Campbell Mikolaj Kasprzak Tamara Broderick 35 15 0 26 Jun 2018