Thoughts on Massively Scalable Gaussian Processes

5 November 2015

Papers citing "Thoughts on Massively Scalable Gaussian Processes"

30 / 30 papers shown

Title
Toward Efficient Kernel-Based Solvers for Nonlinear PDEs Zhitong Xu D. Long Yiming Xu Guang Yang Shandian Zhe Houman Owhadi 85 0 0 15 Oct 2024
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process Hao Yu Kaiyang Guo Mahdi Karami Xi Chen Guojun Zhang Pascal Poupart FedML 78 3 0 13 Jun 2022
Posterior and Computational Uncertainty in Gaussian Processes Jonathan Wenger Geoff Pleiss Marvin Pfortner Philipp Hennig John P. Cunningham 143 20 0 30 May 2022
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes Kyle Hayes Michael W. Fouts Ali Baheri D. Mebane 77 0 0 26 May 2022
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization Jonathan Wenger Geoff Pleiss Philipp Hennig John P. Cunningham Jacob R. Gardner 98 24 0 01 Jul 2021
Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model Rui Meng K. Bouchard AI4TS 50 2 0 25 Jun 2021
Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks J. Fuhg M. Marino N. Bouklas 64 61 0 07 May 2021
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations Luhuan Wu Andrew C. Miller Lauren Anderson Geoff Pleiss David M. Blei John P. Cunningham 70 9 0 28 Feb 2021
The Promises and Pitfalls of Deep Kernel Learning Sebastian W. Ober C. Rasmussen Mark van der Wilk UQCV BDL 82 109 0 24 Feb 2021
Learning ODE Models with Qualitative Structure Using Gaussian Processes Steffen Ridderbusch Christian Offen Sina Ober-Blobaum Paul Goulart 67 15 0 10 Nov 2020
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data Chacha Chen Junjie Liang Fenglong Ma Lucas Glass Jimeng Sun Cao Xiao 71 26 0 22 Oct 2020
Scalable Gaussian Process Regression for Kernels with a Non-Stationary Phase J. Grasshoff Alexandra Jankowski P. Rostalski 48 3 0 25 Dec 2019
Recurrent Attentive Neural Process for Sequential Data Shenghao Qin Jiacheng Zhu Jimmy Qin Wenshuo Wang Ding Zhao BDL AI4TS 81 38 0 17 Oct 2019
Lifelong Bayesian Optimization Yao Zhang James Jordon Ahmed Alaa M. Schaar 123 11 0 29 May 2019
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language Jamie Fairbrother Christopher Nemeth M. Rischard Johanni Brea Thomas Pinder GP VLM 67 24 0 21 Dec 2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration Jacob R. Gardner Geoff Pleiss D. Bindel Kilian Q. Weinberger A. Wilson GP 149 1,105 0 28 Sep 2018
When Gaussian Process Meets Big Data: A Review of Scalable GPs Haitao Liu Yew-Soon Ong Xiaobo Shen Jianfei Cai GP 133 697 0 03 Jul 2018
Constant-Time Predictive Distributions for Gaussian Processes Geoff Pleiss Jacob R. Gardner Kilian Q. Weinberger A. Wilson 67 96 0 16 Mar 2018
State Space Gaussian Processes with Non-Gaussian Likelihood H. Nickisch Arno Solin A. Grigorevskiy GP 76 32 0 13 Feb 2018
Algorithmic Linearly Constrained Gaussian Processes Markus Lange-Hegermann 69 35 0 28 Jan 2018
Gaussian Process Regression for Arctic Coastal Erosion Forecasting Matthew Kupilik F. Witmer E. MacLeod Caixia Wang T. Ravens 49 15 0 04 Dec 2017
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks John Bradshaw A. G. Matthews Zoubin Ghahramani BDL AAML 117 172 0 08 Jul 2017
Scaling up the Automatic Statistician: Scalable Structure Discovery using Gaussian Processes Hyunjik Kim Yee Whye Teh 63 52 0 08 Jun 2017
Bayesian Optimization with Gradients Jian Wu Matthias Poloczek A. Wilson P. Frazier 69 210 0 13 Mar 2017
Variational Fourier features for Gaussian processes J. Hensman N. Durrande Arno Solin VLM 93 202 0 21 Nov 2016
Stochastic Variational Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric Xing BDL 131 267 0 01 Nov 2016
Learning Scalable Deep Kernels with Recurrent Structure Maruan Al-Shedivat A. Wilson Yunus Saatchi Zhiting Hu Eric Xing BDL 102 105 0 27 Oct 2016
Poisson intensity estimation with reproducing kernels Seth Flaxman Yee Whye Teh Dino Sejdinovic 98 48 0 27 Oct 2016
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization Lisha Li Kevin Jamieson Giulia DeSalvo Afshin Rostamizadeh Ameet Talwalkar 246 2,336 0 21 Mar 2016
Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric Xing BDL 261 889 0 06 Nov 2015