Exact Gaussian Processes on a Million Data Points

19 March 2019

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

42 / 142 papers shown

Title
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 18 58 0 08 Nov 2020
Kernel Dependence Network Chieh-Tsai Wu A. Masoomi Arthur Gretton Jennifer Dy 16 0 0 04 Nov 2020
Sample-efficient reinforcement learning using deep Gaussian processes Charles W. L. Gadd Markus Heinonen Harri Lähdesmäki Samuel Kaski GP BDL 31 4 0 02 Nov 2020
Random Fourier Features based SLAM Yermek Kapushev Anastasia Kishkun Gonzalo Ferrer Evgeny Burnaev GP 8 4 0 01 Nov 2020
Scalable Bayesian Optimization with Sparse Gaussian Process Models Ang Yang 16 0 0 26 Oct 2020
Modulating Scalable Gaussian Processes for Expressive Statistical Learning Haitao Liu Yew-Soon Ong Xiaomo Jiang Xiaofang Wang 18 4 0 29 Aug 2020
Locally induced Gaussian processes for large-scale simulation experiments D. Cole R. Christianson R. Gramacy 21 21 0 28 Aug 2020
Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships N. Kudryashova Theoklitos Amvrosiadis Nathalie Dupuy Nathalie L Rochefort A. Onken 19 5 0 03 Aug 2020
Ordinal Regression with Fenton-Wilkinson Order Statistics: A Case Study of an Orienteering Race J. Paakkonen 7 0 0 14 Jul 2020
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes William J. Wilkinson Paul E. Chang Michael Riis Andersen Arno Solin 16 13 0 12 Jul 2020
Data-Driven Discovery of Molecular Photoswitches with Multioutput Gaussian Processes Ryan-Rhys Griffiths Jake L. Greenfield Aditya R. Thawani Arian R. Jamasb Henry B. Moss Anthony Bourached Penelope Jones William McCorkindale Alexander A. Aldrick Matthew J. Fuchter Alpha A. Lee 32 13 0 28 Jun 2020
Uncertainty-Aware (UNA) Bases for Deep Bayesian Regression Using Multi-Headed Auxiliary Networks Sujay Thakur Cooper Lorsung Yaniv Yacoby Finale Doshi-Velez Weiwei Pan BDL UQCV 33 4 0 21 Jun 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization Geoff Pleiss M. Jankowiak David Eriksson Anil Damle Jacob R. Gardner 19 43 0 19 Jun 2020
Auxiliary-task learning for geographic data with autoregressive embeddings Konstantin Klemmer Daniel B. Neill 27 13 0 18 Jun 2020
Kernel methods through the roof: handling billions of points efficiently Giacomo Meanti Luigi Carratino Lorenzo Rosasco Alessandro Rudi 33 114 0 18 Jun 2020
Real-Time Regression with Dividing Local Gaussian Processes Armin Lederer Alejandro Jose Ordóñez Conejo K. Maier Wenxin Xiao Jonas Umlauft Sandra Hirche 11 11 0 16 Jun 2020
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training Diego Granziol S. Zohren Stephen J. Roberts ODL 37 49 0 16 Jun 2020
The Statistical Cost of Robust Kernel Hyperparameter Tuning R. A. Meyer Christopher Musco 16 2 0 14 Jun 2020
Sparse Gaussian Processes via Parametric Families of Compactly-supported Kernels Jarred Barber GP 14 2 0 05 Jun 2020
Autonomous Materials Discovery Driven by Gaussian Process Regression with Inhomogeneous Measurement Noise and Anisotropic Kernels M. Noack G. Doerk Ruipeng Li Jason K. Streit R. Vaia Kevin Yager M. Fukuto 18 70 0 03 Jun 2020
Longitudinal Deep Kernel Gaussian Process Regression Junjie Liang Yanting Wu Dongkuan Xu Vasant Honavar BDL 11 8 0 24 May 2020
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties J. Lindinger David Reeb C. Lippert Barbara Rakitsch BDL UQCV 25 8 0 22 May 2020
Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis M. Cai Michael Shvartsman Anqi Wu Hejia Zhang Xia Zhu AI4CE 35 12 0 11 May 2020
Reinforcement Learning via Gaussian Processes with Neural Network Dual Kernels I. Goumiri Benjamin W. Priest M. Schneider GP BDL 8 6 0 10 Apr 2020
On Negative Transfer and Structure of Latent Functions in Multi-output Gaussian Processes Moyan Li Raed Al Kontar 12 6 0 06 Apr 2020
Linear-time inference for Gaussian Processes on one dimension Jackson Loper David M. Blei John P. Cunningham Liam Paninski 8 16 0 11 Mar 2020
Neural Kernels Without Tangents Vaishaal Shankar Alex Fang Wenshuo Guo Sara Fridovich-Keil Ludwig Schmidt Jonathan Ragan-Kelley Benjamin Recht 25 90 0 04 Mar 2020
Efficiently Sampling Functions from Gaussian Process Posteriors James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 14 163 0 21 Feb 2020
Weakly-supervised Multi-output Regression via Correlated Gaussian Processes Seokhyun Chung Raed Al Kontar Zhenke Wu 21 3 0 19 Feb 2020
$π$ VAE: a stochastic process prior for Bayesian deep learning with MCMC Swapnil Mishra Seth Flaxman Tresnia Berah Harrison Zhu Mikko S. Pakkanen Samir Bhatt BDL 26 3 0 17 Feb 2020
Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes Daniele Gammelli Inon Peled Filipe Rodrigues Dario Pacino H. A. Kurtaran Francisco Câmara Pereira 20 48 0 21 Jan 2020
Bayesian task embedding for few-shot Bayesian optimization Steven Atkinson Sayan Ghosh Natarajan Chennimalai-Kumar Genghis Khan Liping Wang BDL 24 1 0 02 Jan 2020
Scalable Gaussian Process Regression for Kernels with a Non-Stationary Phase J. Grasshoff Alexandra Jankowski P. Rostalski 19 3 0 25 Dec 2019
Fenton-Wilkinson Order Statistics and German Tanks: A Case Study of an Orienteering Relay Race J. Paakkonen 11 0 0 10 Dec 2019
Gaussian Process Priors for View-Aware Inference Wenshuai Zhao Ari Heljakka Arno Solin BDL 23 1 0 06 Dec 2019
Parametric Gaussian Process Regressors M. Jankowiak Geoffrey Pleiss Jacob R. Gardner UQCV 30 5 0 16 Oct 2019
The Renyi Gaussian Process: Towards Improved Generalization Xubo Yue Raed Al Kontar 107 3 0 15 Oct 2019
Fast and Accurate Gaussian Kernel Ridge Regression Using Matrix Decompositions for Preconditioning G. Shabat Era Choshen Dvir Ben-Or Nadav Carmel 7 7 0 25 May 2019
Generalized Variational Inference: Three arguments for deriving new Posteriors Jeremias Knoblauch Jack Jewson Theodoros Damoulas DRL BDL 39 105 0 03 Apr 2019
Change Surfaces for Expressive Multidimensional Changepoints and Counterfactual Prediction William Herlands Daniel B. Neill H. Nickisch A. Wilson OOD 16 2 0 28 Oct 2018
Uncertainty in Neural Networks: Approximately Bayesian Ensembling Tim Pearce Felix Leibfried Alexandra Brintrup Mohamed H. Zaki A. Neely BDL UQCV 20 192 0 12 Oct 2018
When Gaussian Process Meets Big Data: A Review of Scalable GPs Haitao Liu Yew-Soon Ong Xiaobo Shen Jianfei Cai GP 33 681 0 03 Jul 2018