Exact Gaussian Processes on a Million Data Points

19 March 2019

Papers citing "Exact Gaussian Processes on a Million Data Points"

50 / 142 papers shown

Title
Low-Precision Arithmetic for Fast Gaussian Processes Wesley J. Maddox Andres Potapczynski A. Wilson 27 12 0 14 Jul 2022
Memory Safe Computations with XLA Compiler A. Artemev Tilman Roeder Mark van der Wilk 31 8 0 28 Jun 2022
Sparse Kernel Gaussian Processes through Iterative Charted Refinement (ICR) G. Edenhofer R. Leike Philipp Frank T. Ensslin 13 5 0 21 Jun 2022
Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation Sebastian Ament Carla P. Gomes 18 8 0 16 Jun 2022
Posterior and Computational Uncertainty in Gaussian Processes Jonathan Wenger Geoff Pleiss Marvin Pfortner Philipp Hennig John P. Cunningham 80 19 0 30 May 2022
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation Alec M. Dunton Benjamin W. Priest Amanda Muyskens GP 32 3 0 22 May 2022
Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels M. Noack Harinarayan Krishnan M. Risser Kristofer G. Reyes GP 77 16 0 18 May 2022
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel Ziyang Jiang Tongshu Zheng Yiling Liu David Carlson 32 4 0 15 May 2022
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning Veit Wild Robert Hu Dino Sejdinovic BDL 51 11 0 12 May 2022
Inducing Gaussian Process Networks Alessandro Tibo Thomas D. Nielsen BDL 14 1 0 21 Apr 2022
Safe Active Learning for Multi-Output Gaussian Processes Cen-You Li Barbara Rakitsch Christoph Zimmer UQCV 30 17 0 28 Mar 2022
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control Abdolreza Taheri Joni Pajarinen R. Ghabcheloo GP 19 3 0 28 Feb 2022
Parallel MCMC Without Embarrassing Failures Daniel Augusto R. M. A. de Souza Diego Mesquita Samuel Kaski Luigi Acerbi 42 11 0 22 Feb 2022
Adaptive Cholesky Gaussian Processes Simon Bartels Kristoffer Stensbo-Smidt Pablo Moreno-Muñoz Wouter Boomsma J. Frellsen Søren Hauberg 33 3 0 22 Feb 2022
Variational Nearest Neighbor Gaussian Process Luhuan Wu Geoff Pleiss John P. Cunningham BDL 29 15 0 03 Feb 2022
Giga-scale Kernel Matrix Vector Multiplication on GPU Robert Hu Siu Lun Chau Dino Sejdinovic J. Glaunès 29 2 0 02 Feb 2022
An Overview of Uncertainty Quantification Methods for Infinite Neural Networks Florian Juengermann Maxime Laasri Marius Merkle BDL 11 0 0 13 Jan 2022
When are Iterative Gaussian Processes Reliably Accurate? Wesley J. Maddox Sanyam Kapoor A. Wilson 35 10 0 31 Dec 2021
Correlated Product of Experts for Sparse Gaussian Process Regression Manuel Schürch Dario Azzimonti A. Benavoli Marco Zaffalon 24 12 0 17 Dec 2021
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits Hao Chen Lili Zheng Raed Al Kontar Garvesh Raskutti 24 3 0 19 Nov 2021
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes Hugh Dance Brooks Paige GP 20 10 0 08 Nov 2021
Spatio-Temporal Variational Gaussian Processes Oliver Hamelijnck William J. Wilkinson Niki A. Loppi Arno Solin Theodoros Damoulas AI4TS 19 31 0 02 Nov 2021
Variational Gaussian Processes: A Functional Analysis View Veit Wild George Wynne GP 46 5 0 25 Oct 2021
Bayesian Meta-Learning Through Variational Gaussian Processes Vivek Myers Nikhil Sardana BDL UQCV 11 4 0 21 Oct 2021
A portfolio approach to massively parallel Bayesian optimization M. Binois Nicholson T. Collier J. Ozik 27 9 0 18 Oct 2021
Incremental Ensemble Gaussian Processes Qin Lu G. V. Karanikolas G. Giannakis 53 24 0 13 Oct 2021
Molecular Energy Learning Using Alternative Blackbox Matrix-Matrix Multiplication Algorithm for Exact Gaussian Process Jiace Sun Lixue Cheng Thomas F. Miller 34 3 0 20 Sep 2021
Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors Cédric Travelletti D. Ginsbourger N. Linde 30 3 0 08 Sep 2021
Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models Zhiliang Wu Yinchong Yang Peter A. Fasching Volker Tresp BDL 21 10 0 26 Jul 2021
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization Jonathan Wenger Geoff Pleiss Philipp Hennig John P. Cunningham Jacob R. Gardner 22 24 0 01 Jul 2021
Bayesian Optimization with High-Dimensional Outputs Wesley J. Maddox Maximilian Balandat A. Wilson E. Bakshy UQCV 24 50 0 24 Jun 2021
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes Sanyam Kapoor Marc Finzi Ke Alexander Wang A. Wilson 41 11 0 12 Jun 2021
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition Shengyang Sun Jiaxin Shi A. Wilson Roger C. Grosse BDL 17 6 0 10 Jun 2021
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data Jens Petersen Gregor Koehler David Zimmerer Fabian Isensee Paul F. Jäger Klaus H. Maier-Hein BDL AI4TS 31 3 0 09 Jun 2021
The Fast Kernel Transform J. Ryan Sebastian Ament Carla P. Gomes Anil Damle 18 8 0 08 Jun 2021
Scalable Cross Validation Losses for Gaussian Process Models M. Jankowiak Geoff Pleiss 26 6 0 24 May 2021
Understanding Uncertainty in Bayesian Deep Learning Cooper Lorsung BDL UQCV 12 0 0 21 May 2021
Deep Neural Networks as Point Estimates for Deep Gaussian Processes Vincent Dutordoir J. Hensman Mark van der Wilk Carl Henrik Ek Zoubin Ghahramani N. Durrande BDL UQCV 18 30 0 10 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
Affective Processes: stochastic modelling of temporal context for emotion and facial expression recognition Enrique Sanchez M. Tellamekala M. Valstar Georgios Tzimiropoulos CVBM 40 28 0 24 Mar 2021
Kernel Interpolation for Scalable Online Gaussian Processes Samuel Stanton Wesley J. Maddox Ian A. Delbridge A. Wilson GP 14 28 0 02 Mar 2021
Generative Particle Variational Inference via Estimation of Functional Gradients Neale Ratzlaff Qinxun Bai Fuxin Li Wenyuan Xu BDL DRL 23 0 0 01 Mar 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 27 8 0 28 Feb 2021
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients A. Artemev David R. Burt Mark van der Wilk 23 18 0 16 Feb 2021
Healing Products of Gaussian Processes Samuel N. Cohen R. Mbuvha T. Marwala M. Deisenroth GP 21 0 0 14 Feb 2021
Appearance of Random Matrix Theory in Deep Learning Nicholas P. Baskerville Diego Granziol J. Keating 15 11 0 12 Feb 2021
Bias-Free Scalable Gaussian Processes via Randomized Truncations Andres Potapczynski Luhuan Wu D. Biderman Geoff Pleiss John P. Cunningham 26 19 0 12 Feb 2021
Interpolating Classifiers Make Few Mistakes Tengyuan Liang Benjamin Recht 14 28 0 28 Jan 2021
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games Roxana Rădulescu T. Verstraeten Yijie Zhang Patrick Mannion D. Roijers A. Nowé 25 14 0 14 Nov 2020