Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs

9 March 2015

Papers citing "Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs"

11 / 11 papers shown

Title
Adaptive RKHS Fourier Features for Compositional Gaussian Process Models Xinxing Shi Thomas Baldwin-McDonald Mauricio A. Álvarez 79 0 0 01 Jul 2024
Gaussian Process-Gated Hierarchical Mixtures of Experts Yuhao Liu Marzieh Ajirak P. Djuric MoE 16 1 0 09 Feb 2023
Incremental Ensemble Gaussian Processes Qin Lu G. V. Karanikolas G. Giannakis 53 23 0 13 Oct 2021
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming Gabriel Riutort-Mayol Paul-Christian Bürkner Michael R. Andersen Arno Solin Aki Vehtari 32 68 0 23 Apr 2020
Neural Non-Stationary Spectral Kernel Sami Remes Markus Heinonen Samuel Kaski BDL 16 9 0 27 Nov 2018
Harmonizable mixture kernels with variational Fourier features Zheyan Shen Markus Heinonen Samuel Kaski 17 17 0 10 Oct 2018
Variational Implicit Processes Chao Ma Yingzhen Li José Miguel Hernández-Lobato BDL 24 68 0 06 Jun 2018
Efficient Bayesian Inference for a Gaussian Process Density Model Christian Donner Manfred Opper 29 14 0 29 May 2018
Random Feature Expansions for Deep Gaussian Processes Kurt Cutajar Edwin V. Bonilla Pietro Michiardi Maurizio Filippone BDL 14 142 0 14 Oct 2016
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes T. Nickson Tom Gunter C. Lloyd Michael A. Osborne Stephen J. Roberts 16 21 0 27 Oct 2015
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference Y. Gal Zoubin Ghahramani UQCV BDL 202 745 0 06 Jun 2015