A Framework for Evaluating Approximation Methods for Gaussian Process Regression

29 May 2012

Papers citing "A Framework for Evaluating Approximation Methods for Gaussian Process Regression"

42 / 42 papers shown

Title
Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functions Gledson Rodrigo Tondo I. Kavrakov Guido Morgenthal 52 2 0 13 Mar 2025
Scalable Random Feature Latent Variable Models Ying Li Zhidi Lin Yuhao Liu Michael Minyi Zhang Pablo Martínez Olmos P. Djuric BDL DRL 30 0 0 23 Oct 2024
A Framework of Zero-Inflated Bayesian Negative Binomial Regression Models For Spatiotemporal Data Qing He Hsin-Hsiung Huang 13 4 0 06 Feb 2024
Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression Robert Allison Anthony Stephenson F. Samuel Edward O. Pyzer-Knapp UQCV 17 3 0 26 Jun 2023
Query-Efficient Black-Box Red Teaming via Bayesian Optimization Deokjae Lee JunYeong Lee Jung-Woo Ha Jin-Hwa Kim Sang-Woo Lee Hwaran Lee Hyun Oh Song AAML 19 23 0 27 May 2023
A Global-Local Approximation Framework for Large-Scale Gaussian Process Modeling Akhil Vakayil Roshan Joseph 23 2 0 17 May 2023
Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization Deokjae Lee Seungyong Moon Junhyeok Lee Hyun Oh Song AAML 15 38 0 17 Jun 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 27 0 0 26 May 2022
Adaptive Cholesky Gaussian Processes Simon Bartels Kristoffer Stensbo-Smidt Pablo Moreno-Muñoz Wouter Boomsma J. Frellsen Søren Hauberg 22 3 0 22 Feb 2022
Scalable3-BO: Big Data meets HPC - A scalable asynchronous parallel high-dimensional Bayesian optimization framework on supercomputers Anh Tran 6 0 0 12 Aug 2021
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition Simon Bartels Wouter Boomsma J. Frellsen Damien Garreau 24 4 0 22 Jul 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 30 25 0 26 Jun 2021
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting Kai Chen Twan van Laarhoven E. Marchiori AI4TS 31 8 0 08 Nov 2020
Aggregating Dependent Gaussian Experts in Local Approximation Hamed Jalali Gjergji Kasneci 14 4 0 17 Oct 2020
A Bayesian Nonparametric Analysis of the 2003 Outbreak of Highly Pathogenic Avian Influenza in the Netherlands Rowland G. Seymour T. Kypraios P. O’Neill T. Hagenaars 4 5 0 09 Sep 2020
Examining the Role of Mood Patterns in Predicting Self-Reported Depressive symptoms L. L. Chen Walid Magdy H. Whalley M. Wolters 13 11 0 14 Jun 2020
Fast increased fidelity approximate Gibbs samplers for Bayesian Gaussian process regression Kelly R. Moran M. Wheeler 16 4 0 11 Jun 2020
A Survey of Bayesian Statistical Approaches for Big Data Farzana Jahan Insha Ullah Kerrie Mengersen 37 14 0 08 Jun 2020
aphBO-2GP-3B: A budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture Anh Tran J. Furlan T. Wildey S. McCann K. Pagalthivarthi R. Visintainer 14 9 0 20 Mar 2020
Development of modeling and control strategies for an approximated Gaussian process Shisheng Cui Chia-Jung Chang 18 0 0 12 Feb 2020
Conjugate Gradients for Kernel Machines Simon Bartels Philipp Hennig 16 4 0 14 Nov 2019
A Low Rank Gaussian Process Prediction Model for Very Large Datasets R. Rivera 17 1 0 09 Jun 2019
Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and Verification Yue Xu Feng Yin Wenjun Xu Jiaru Lin Shuguang Cui 27 99 0 13 Feb 2019
Large-scale Heteroscedastic Regression via Gaussian Process Haitao Liu Yew-Soon Ong Jianfei Cai BDL 19 26 0 03 Nov 2018
Understanding and Comparing Scalable Gaussian Process Regression for Big Data Haitao Liu Jianfei Cai Yew-Soon Ong Yi Wang 21 24 0 03 Nov 2018
Multi-Output Convolution Spectral Mixture for Gaussian Processes Kai Chen Twan van Laarhoven P. Groot Jinsong Chen E. Marchiori 14 11 0 07 Aug 2018
When Gaussian Process Meets Big Data: A Review of Scalable GPs Haitao Liu Yew-Soon Ong Xiaobo Shen Jianfei Cai GP 11 681 0 03 Jul 2018
A Data-Driven Approach to Dynamically Adjust Resource Allocation for Compute Clusters Francesco Pace Dimitrios Milios D. Carra D. Venzano Pietro Michiardi 6 4 0 01 Jul 2018
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression Haitao Liu Jianfei Cai Yi Wang Yew-Soon Ong 12 83 0 03 Jun 2018
Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference B. Thijssen L. Wessels 35 8 0 12 Dec 2017
Cluster-based Kriging Approximation Algorithms for Complexity Reduction Bas van Stein Hao Wang W. Kowalczyk M. Emmerich Thomas Bäck 33 45 0 04 Feb 2017
Overlapping Cover Local Regression Machines Mohamed Elhoseiny Ahmed Elgammal 36 0 0 05 Jan 2017
Exploring Prediction Uncertainty in Machine Translation Quality Estimation Daniel Beck Lucia Specia Trevor Cohn UQLM 13 18 0 30 Jun 2016
Preconditioning Kernel Matrices Kurt Cutajar Michael A. Osborne John P. Cunningham Maurizio Filippone 18 72 0 22 Feb 2016
System Identification through Online Sparse Gaussian Process Regression with Input Noise Hildo Bijl Thomas B. Schon J. Wingerden M. Verhaegen 30 41 0 29 Jan 2016
Gaussian Process Random Fields David A. Moore Stuart J. Russell GP 17 19 0 31 Oct 2015
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) A. Wilson H. Nickisch GP 32 508 0 03 Mar 2015
Distributed Gaussian Processes M. Deisenroth Jun Wei Ng GP 24 340 0 10 Feb 2015
Fast Direct Methods for Gaussian Processes Sivaram Ambikasaran D. Foreman-Mackey L. Greengard D. Hogg M. O’Neil 46 378 0 24 Mar 2014
Robust and Scalable Bayes via a Median of Subset Posterior Measures Stanislav Minsker Sanvesh Srivastava Lizhen Lin David B. Dunson 43 109 0 11 Mar 2014
Hilbert Space Methods for Reduced-Rank Gaussian Process Regression Arno Solin Simo Särkkä 63 211 0 21 Jan 2014
Efficient Optimization for Sparse Gaussian Process Regression Yanshuai Cao Marcus A. Brubaker David J. Fleet Aaron Hertzmann 38 63 0 22 Oct 2013