Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes

8 June 2017

Papers citing "Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes"

13 / 13 papers shown

Title
Trace Encoding in Process Mining: a survey and benchmarking Sylvio Barbon Junior Paolo Ceravolo R. Oyamada G. Tavares AI4TS 31 20 0 05 Jan 2023
Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations Andreas Besginow Markus Lange-Hegermann 32 11 0 26 Aug 2022
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
Surrogate modeling for Bayesian optimization beyond a single Gaussian process Qin Lu Konstantinos D. Polyzos Bingcong Li G. Giannakis GP 20 18 0 27 May 2022
Adaptive Cholesky Gaussian Processes Simon Bartels Kristoffer Stensbo-Smidt Pablo Moreno-Muñoz Wouter Boomsma J. Frellsen Søren Hauberg 33 3 0 22 Feb 2022
Incremental Ensemble Gaussian Processes Qin Lu G. V. Karanikolas G. Giannakis 53 24 0 13 Oct 2021
Online structural kernel selection for mobile health Eura Shin Pedja Klasnja S. Murphy Finale Doshi-Velez 22 1 0 21 Jul 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 31 124 0 14 May 2021
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients A. Artemev David R. Burt Mark van der Wilk 23 18 0 16 Feb 2021
Time series forecasting with Gaussian Processes needs priors Giorgio Corani A. Benavoli Marco Zaffalon GP AI4TS 15 28 0 17 Sep 2020
Convergence of Sparse Variational Inference in Gaussian Processes Regression David R. Burt C. Rasmussen Mark van der Wilk 29 69 0 01 Aug 2020
Large Scale Tensor Regression using Kernels and Variational Inference Robert Hu Geoff K. Nicholls Dino Sejdinovic 15 4 0 11 Feb 2020
Learning Invariances using the Marginal Likelihood Mark van der Wilk Matthias Bauer S. T. John J. Hensman 36 83 0 16 Aug 2018