Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations

Conference on Uncertainty in Artificial Intelligence (UAI), 2013

24 May 2013

Papers citing "Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations"

28 / 28 papers shown

Title
Bayesian Optimization for Dynamic Pricing and Learning Anush Anand Pranav Agrawal Tejas Bodas 118 0 0 14 Oct 2025
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO Algorithm Buse Sibel Korkmaz Marta Zagórowska Mehmet Mercangöz 170 2 0 10 Nov 2022
Low-Precision Arithmetic for Fast Gaussian ProcessesConference on Uncertainty in Artificial Intelligence (UAI), 2022 Wesley J. Maddox Andres Potapczynski A. Wilson 138 14 0 14 Jul 2022
Bayesian Optimization under Stochastic Delayed FeedbackInternational Conference on Machine Learning (ICML), 2022 Arun Verma Zhongxiang Dai Bryan Kian Hsiang Low 218 15 0 19 Jun 2022
Convolutional Normalizing Flows for Deep Gaussian ProcessesIEEE International Joint Conference on Neural Network (IJCNN), 2021 Haibin Yu Dapeng Liu Yizhou Chen K. H. Low Patrick Jaillet BDL 176 6 0 17 Apr 2021
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian ProcessesNeural Information Processing Systems (NeurIPS), 2020 Q. Hoang T. Hoang Hai Pham David P. Woodruff 158 6 0 17 Nov 2020
Variational Bayesian UnlearningNeural Information Processing Systems (NeurIPS), 2020 Q. Nguyen Bryan Kian Hsiang Low Patrick Jaillet BDL MU 236 151 0 24 Oct 2020
Private Outsourced Bayesian OptimizationInternational Conference on Machine Learning (ICML), 2020 D. Kharkovskii Zhongxiang Dai K. H. Low 184 25 0 24 Oct 2020
Federated Bayesian Optimization via Thompson SamplingNeural Information Processing Systems (NeurIPS), 2020 Zhongxiang Dai K. H. Low Patrick Jaillet FedML 415 132 0 20 Oct 2020
Locally induced Gaussian processes for large-scale simulation experimentsStatistics and computing (Stat. Comput.), 2020 D. Cole R. Christianson R. Gramacy 251 23 0 28 Aug 2020
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games Zhongxiang Dai Yizhou Chen K. H. Low Patrick Jaillet Teck-Hua Ho 184 28 0 30 Jun 2020
Nonmyopic Gaussian Process Optimization with Macro-ActionsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020 D. Kharkovskii Chun Kai Ling K. H. Low 196 18 0 22 Feb 2020
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process RegressionAAAI Conference on Artificial Intelligence (AAAI), 2019 T. Teng Jie Chen Yehong Zhang K. H. Low BDL 184 24 0 05 Dec 2019
Implicit Posterior Variational Inference for Deep Gaussian ProcessesNeural Information Processing Systems (NeurIPS), 2019 Haibin Yu Yizhou Chen Zhongxiang Dai K. H. Low Patrick Jaillet 205 44 0 26 Oct 2019
Sparse Additive Gaussian Process RegressionJournal of machine learning research (JMLR), 2019 Hengrui Luo Giovanni Nattino M. Pratola 244 18 0 23 Aug 2019
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 395 801 0 03 Jul 2018
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems T. Hoang Q. Hoang K. H. Low Jonathan P. How 201 6 0 23 May 2018
Decentralized High-Dimensional Bayesian Optimization with Factor Graphs T. Hoang Q. Hoang Ruofei Ouyang K. H. Low 219 58 0 19 Nov 2017
Gaussian Process Decentralized Data Fusion Meets Transfer Learning in Large-Scale Distributed Cooperative Perception Ruofei Ouyang K. H. Low FedML 168 26 0 16 Nov 2017
Stochastic Variational Inference for Bayesian Sparse Gaussian Process Regression Haibin Yu T. Hoang K. H. Low Patrick Jaillet BDL 339 24 0 01 Nov 2017
Forecasting of commercial sales with large scale Gaussian Processes Rodrigo Rivera Evgeny Burnaev 141 21 0 16 Sep 2017
Patchwork Kriging for Large-scale Gaussian Process RegressionJournal of machine learning research (JMLR), 2017 Chiwoo Park D. Apley 217 80 0 23 Jan 2017
A Generalized Stochastic Variational Bayesian Hyperparameter Learning Framework for Sparse Spectrum Gaussian Process Regression Q. Hoang T. Hoang K. H. Low 162 40 0 18 Nov 2016
Near-Optimal Active Learning of Multi-Output Gaussian Processes Yehong Zhang T. Hoang K. H. Low Mohan Kankanhalli 166 39 0 21 Nov 2015
Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond Chun Kai Ling K. H. Low Patrick Jaillet 163 79 0 21 Nov 2015
Identifying Reliable Annotations for Large Scale Image Segmentation Alexander Kolesnikov Christoph H. Lampert 106 0 0 28 Apr 2015
Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov ApproximationAAAI Conference on Artificial Intelligence (AAAI), 2014 K. H. Low J. Yu Jie Chen Patrick Jaillet 166 54 0 17 Nov 2014
GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation ModelAAAI Conference on Artificial Intelligence (AAAI), 2014 Nuo Xu K. H. Low Jie Chen Keng Kiat Lim Etkin Baris Ozgul 230 49 0 21 Apr 2014