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

Title
nuGPR: GPU-Accelerated Gaussian Process Regression with Iterative Algorithms and Low-Rank ApproximationsSIAM Journal on Scientific Computing (SISC), 2025 Ziqi Zhao Vivek Sarin 72 0 0 14 Oct 2025
Fast Gaussian process inference by exact Matérn kernel decomposition N. Langrené X. Warin Pierre Gruet 120 0 0 03 Aug 2025
A Partitioned Sparse Variational Gaussian Process for Fast, Distributed Spatial Modeling M. Grosskopf Kellin Rumsey Ayan Biswas E. Lawrence 95 0 0 22 Jul 2025
Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functionsMechanical systems and signal processing (MSSP), 2025 Gledson Rodrigo Tondo I. Kavrakov Guido Morgenthal 167 2 0 13 Mar 2025
Scalable Random Feature Latent Variable ModelsIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024 Ying Li Zhidi Lin Yuhao Liu Michael Minyi Zhang Pablo M. Olmos Petar M. Djurić BDL DRL 238 0 0 23 Oct 2024
A Framework of Zero-Inflated Bayesian Negative Binomial Regression Models For Spatiotemporal Data Qing He Hsin-Hsiung Huang 205 5 0 06 Feb 2024
Sparse Cholesky factorization by greedy conditional selection Stephen Huan J. Guinness Matthias Katzfuss H. Owhadi Florian Schäfer 84 0 0 21 Jul 2023
Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process RegressionNeural Information Processing Systems (NeurIPS), 2023 Robert Allison Anthony Stephenson F. Samuel Edward O. Pyzer-Knapp UQCV 217 5 0 26 Jun 2023
Query-Efficient Black-Box Red Teaming via Bayesian OptimizationAnnual Meeting of the Association for Computational Linguistics (ACL), 2023 Deokjae Lee JunYeong Lee Jung-Woo Ha Jin-Hwa Kim Sang-Woo Lee Hwaran Lee Hyun Oh Song AAML 160 28 0 27 May 2023
A Global-Local Approximation Framework for Large-Scale Gaussian Process Modeling Akhil Vakayil Roshan Joseph 172 5 0 17 May 2023
Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian OptimizationInternational Conference on Machine Learning (ICML), 2022 Deokjae Lee Seungyong Moon Junhyeok Lee Hyun Oh Song AAML 154 47 0 17 Jun 2022
Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processesPLoS ONE (PLoS ONE), 2022 Kyle Hayes Michael W. Fouts Ali Baheri D. Mebane 327 0 0 26 May 2022
Adaptive Cholesky Gaussian ProcessesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022 Simon Bartels Kristoffer Stensbo-Smidt Pablo Moreno-Muñoz Wouter Boomsma J. Frellsen Søren Hauberg 216 4 0 22 Feb 2022
Scalable3-BO: Big Data meets HPC - A scalable asynchronous parallel high-dimensional Bayesian optimization framework on supercomputersConference on Computability in Europe (CiE), 2021 Anh Tran 143 2 0 12 Aug 2021
Kernel-Matrix Determinant Estimates from stopped Cholesky DecompositionJournal of machine learning research (JMLR), 2021 Simon Bartels Wouter Boomsma J. Frellsen Damien Garreau 180 6 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 190 29 0 26 Jun 2021
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting Kai Chen Twan van Laarhoven E. Marchiori AI4TS 253 9 0 08 Nov 2020
Aggregating Dependent Gaussian Experts in Local ApproximationInternational Conference on Pattern Recognition (ICPR), 2020 Hamed Jalali Gjergji Kasneci 146 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 101 5 0 09 Sep 2020
Examining the Role of Mood Patterns in Predicting Self-Reported Depressive symptomsWeb Science Conference (WebSci), 2020 L. L. Chen Walid Magdy H. Whalley M. Wolters 91 12 0 14 Jun 2020
Fast increased fidelity approximate Gibbs samplers for Bayesian Gaussian process regressionJournal of The Royal Statistical Society Series B-statistical Methodology (JRSSB), 2020 Kelly R. Moran M. Wheeler 114 4 0 11 Jun 2020
A Survey of Bayesian Statistical Approaches for Big Data Farzana Jahan Insha Ullah Kerrie Mengersen 221 15 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 193 9 0 20 Mar 2020
Development of modeling and control strategies for an approximated Gaussian process Shisheng Cui Chia-Jung Chang 153 0 0 12 Feb 2020
Conjugate Gradients for Kernel MachinesJournal of machine learning research (JMLR), 2019 Simon Bartels Philipp Hennig 162 5 0 14 Nov 2019
A Low Rank Gaussian Process Prediction Model for Very Large Datasets R. Rivera 94 1 0 09 Jun 2019
Wireless Traffic Prediction with Scalable Gaussian Process: Framework, Algorithms, and VerificationIEEE Journal on Selected Areas in Communications (JSAC), 2019 Yue Xu Feng Yin Wenjun Xu Jiaru Lin Shuguang Cui 171 106 0 13 Feb 2019
Large-scale Heteroscedastic Regression via Gaussian Process Haitao Liu Yew-Soon Ong Jianfei Cai BDL 244 30 0 03 Nov 2018
Understanding and Comparing Scalable Gaussian Process Regression for Big Data Haitao Liu Jianfei Cai Yew-Soon Ong Yi Wang 123 30 0 03 Nov 2018
Multi-Output Convolution Spectral Mixture for Gaussian Processes Kai Chen Twan van Laarhoven P. Groot Jinsong Chen E. Marchiori 381 11 0 07 Aug 2018
When Gaussian Process Meets Big Data: A Review of Scalable GPsIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2018 Haitao Liu Yew-Soon Ong Xiaobo Shen Jianfei Cai GP 363 788 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 132 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 167 89 0 03 Jun 2018
Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference B. Thijssen L. Wessels 214 9 0 12 Dec 2017
Cluster-based Kriging Approximation Algorithms for Complexity Reduction Bas van Stein Hao Wang W. Kowalczyk M. Emmerich Thomas Bäck 169 47 0 04 Feb 2017
Overlapping Cover Local Regression Machines Mohamed Elhoseiny Ahmed Elgammal 151 0 0 05 Jan 2017
Exploring Prediction Uncertainty in Machine Translation Quality EstimationConference on Computational Natural Language Learning (CoNLL), 2016 Daniel Beck Lucia Specia Trevor Cohn UQLM 166 19 0 30 Jun 2016
Preconditioning Kernel Matrices Kurt Cutajar Michael A. Osborne John P. Cunningham Maurizio Filippone 306 77 0 22 Feb 2016
System Identification through Online Sparse Gaussian Process Regression with Input Noise Hildo Bijl Thomas B. Schon J. Wingerden M. Verhaegen 235 45 0 29 Jan 2016
Gaussian Process Random Fields David A. Moore Stuart J. Russell GP 134 19 0 31 Oct 2015
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) A. Wilson H. Nickisch GP 236 539 0 03 Mar 2015
Distributed Gaussian Processes M. Deisenroth Jun Wei Ng GP 330 366 0 10 Feb 2015
Fast Direct Methods for Gaussian ProcessesIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2014 Sivaram Ambikasaran D. Foreman-Mackey L. Greengard D. Hogg M. O’Neil 289 396 0 24 Mar 2014
Robust and Scalable Bayes via a Median of Subset Posterior MeasuresJournal of machine learning research (JMLR), 2014 Stanislav Minsker Sanvesh Srivastava Lizhen Lin David B. Dunson 414 113 0 11 Mar 2014
Hilbert Space Methods for Reduced-Rank Gaussian Process RegressionStatistics and computing (Stat Comput), 2014 Arno Solin Simo Särkkä 522 242 0 21 Jan 2014
Efficient Optimization for Sparse Gaussian Process RegressionIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2013 Yanshuai Cao Marcus A. Brubaker David J. Fleet Aaron Hertzmann 287 67 0 22 Oct 2013