When Gaussian Process Meets Big Data: A Review of Scalable GPs

3 July 2018

Jianfei Cai

Papers citing "When Gaussian Process Meets Big Data: A Review of Scalable GPs"

33 / 83 papers shown

Title
Revisiting Memory Efficient Kernel Approximation: An Indefinite Learning Perspective Simon Heilig Maximilian Münch Frank-Michael Schleif 31 1 0 18 Dec 2021
BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameters Sami Alabed Eiko Yoneki 17 7 0 16 Dec 2021
Comparing Machine Learning and Interpolation Methods for Loop-Level Calculations Ibrahim Chahrour J. Wells 10 12 0 29 Nov 2021
Non-separable Spatio-temporal Graph Kernels via SPDEs A. Nikitin S. T. John Arno Solin Samuel Kaski AI4TS 33 17 0 16 Nov 2021
Contraction rates for sparse variational approximations in Gaussian process regression D. Nieman Botond Szabó Harry Van Zanten 54 17 0 22 Sep 2021
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes Jongseo Lee Jianxiang Feng Matthias Humt M. Müller Rudolph Triebel UQCV 50 21 0 20 Sep 2021
Scalable Multi-Task Gaussian Processes with Neural Embedding of Coregionalization Haitao Liu Jiaqi Ding Xinyu Xie Xiaomo Jiang Yusong Zhao Xiaofang Wang BDL 34 14 0 20 Sep 2021
Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors Liwei Wang Suraj Yerramilli Akshay Iyer D. Apley Ping Zhu Wei Chen 38 25 0 26 Jun 2021
Deep Gaussian Processes: A Survey Kalvik Jakkala AI4CE GP BDL 29 19 0 21 Jun 2021
Adaptive machine learning for protein engineering B. Hie Kevin Kaichuang Yang 32 80 0 10 Jun 2021
Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems Haitao Liu Changjun Liu Xiaomo Jiang Xudong Chen Shuhua Yang Xiaofang Wang BDL AI4TS 47 2 0 03 Jun 2021
Scalable Cross Validation Losses for Gaussian Process Models M. Jankowiak Geoff Pleiss 26 6 0 24 May 2021
Lightweight Distributed Gaussian Process Regression for Online Machine Learning Zhenyuan Yuan Minghui Zhu 21 4 0 11 May 2021
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure Samuel Kim Peter Y. Lu Charlotte Loh Jamie Smith Jasper Snoek M. Soljavcić BDL AI4CE 148 17 0 23 Apr 2021
Data-Driven Robust Barrier Functions for Safe, Long-Term Operation Y. Emam Paul Glotfelter S. Wilson Gennaro Notomista M. Egerstedt 13 25 0 15 Apr 2021
ARXON: A Framework for Approximate Communication over Photonic Networks-on-Chip Febin P. Sunny Asif Mirza Ishan G. Thakkar Mahdi Nikdast S. Pasricha 16 17 0 16 Mar 2021
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks Daniel Schwalbe-Koda Aik Rui Tan Rafael Gómez-Bombarelli AAML 31 60 0 27 Jan 2021
Transferring model structure in Bayesian transfer learning for Gaussian process regression Milan Papez A. Quinn 24 11 0 18 Jan 2021
Twin Neural Network Regression S. J. Wetzel Kevin Ryczko R. Melko Isaac Tamblyn UQCV 28 11 0 29 Dec 2020
UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data Chacha Chen Junjie Liang Fenglong Ma Lucas Glass Jimeng Sun Cao Xiao 21 26 0 22 Oct 2020
Lateral Force Prediction using Gaussian Process Regression for Intelligent Tire Systems B. Barbosa N. Xu H. Askari A. Khajepour 6 26 0 25 Sep 2020
Planning from Images with Deep Latent Gaussian Process Dynamics Nathanael Bosch Jan Achterhold Laura Leal-Taixé J. Stückler 20 1 0 07 May 2020
Scaled Vecchia approximation for fast computer-model emulation Matthias Katzfuss J. Guinness E. Lawrence 19 40 0 01 May 2020
Gaussian Process Learning-based Probabilistic Optimal Power Flow Parikshit Pareek H. Nguyen 16 35 0 16 Apr 2020
The Renyi Gaussian Process: Towards Improved Generalization Xubo Yue Raed Al Kontar 107 3 0 15 Oct 2019
Large-scale Environmental Data Science with ExaGeoStatR Sameh Abdulah Yuxiao Li JIAN-PENG Cao Hatem Ltaief David E. Keyes M. Genton Ying Sun 25 10 0 23 Jul 2019
Exact Gaussian Processes on a Million Data Points Ke Alexander Wang Geoff Pleiss Jacob R. Gardner Stephen Tyree Kilian Q. Weinberger A. Wilson GP 15 226 0 19 Mar 2019
GPdoemd: a Python package for design of experiments for model discrimination Simon Olofsson Lukas Hebing Sebastian Niedenführ M. Deisenroth Ruth Misener 27 18 0 05 Oct 2018
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks John Bradshaw A. G. Matthews Zoubin Ghahramani BDL AAML 72 171 0 08 Jul 2017
Manifold Gaussian Processes for Regression Roberto Calandra Jan Peters C. Rasmussen M. Deisenroth 92 271 0 24 Feb 2014
Local Gaussian process approximation for large computer experiments R. Gramacy D. Apley 127 392 0 02 Mar 2013
Variable noise and dimensionality reduction for sparse Gaussian processes Edward Snelson Zoubin Ghahramani 87 79 0 27 Jun 2012
A Framework for Evaluating Approximation Methods for Gaussian Process Regression Krzysztof Chalupka Christopher K. I. Williams Iain Murray GP 71 169 0 29 May 2012