Gaussian Processes for Big Data

26 September 2013

Papers citing "Gaussian Processes for Big Data"

50 / 604 papers shown

Title
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning Chao Du Zhifeng Gao Shuo Yuan Lining Gao Z. Li Yifan Zeng Xiaoqiang Zhu Jian Xu Kun Gai Kuang-chih Lee 96 18 0 25 Nov 2020
Robust Gaussian Process Regression Based on Iterative Trimming Zhaozhou Li Lu Li Z. Shao 28 23 0 22 Nov 2020
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes Q. Hoang T. Hoang Hai Pham David P. Woodruff 25 5 0 17 Nov 2020
Cluster-Specific Predictions with Multi-Task Gaussian Processes Arthur Leroy Pierre Latouche Benjamin Guedj S. Gey 30 4 0 16 Nov 2020
Sparse within Sparse Gaussian Processes using Neighbor Information Gia-Lac Tran Dimitrios Milios Pietro Michiardi Maurizio Filippone 43 16 0 10 Nov 2020
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 111 61 0 08 Nov 2020
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations? Chaoqi Wang Shengyang Sun Roger C. Grosse UQCV 71 25 0 06 Nov 2020
Transforming Gaussian Processes With Normalizing Flows Juan Maroñas Oliver Hamelijnck Jeremias Knoblauch Theodoros Damoulas 105 34 0 03 Nov 2020
Inter-domain Deep Gaussian Processes Tim G. J. Rudner Dino Sejdinovic Yarin Gal 79 11 0 01 Nov 2020
Matérn Gaussian Processes on Graphs Viacheslav Borovitskiy I. Azangulov Alexander Terenin P. Mostowsky M. Deisenroth N. Durrande 57 82 0 29 Oct 2020
A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance Sudeep Salgia Sattar Vakili Qing Zhao 110 34 0 27 Oct 2020
Scalable Gaussian Process Variational Autoencoders Metod Jazbec Matthew Ashman Vincent Fortuin Michael Pearce Stephan Mandt Gunnar Rätsch DRL BDL 89 29 0 26 Oct 2020
Variational Bayesian Unlearning Q. Nguyen Bryan Kian Hsiang Low Patrick Jaillet BDL MU 98 128 0 24 Oct 2020
Semi-parametric $γ$ -ray modeling with Gaussian processes and variational inference S. Mishra-Sharma Kyle Cranmer MedIm 102 7 0 20 Oct 2020
Probabilistic selection of inducing points in sparse Gaussian processes Anders Kirk Uhrenholt V. Charvet B. S. Jensen 54 13 0 19 Oct 2020
Aggregating Dependent Gaussian Experts in Local Approximation Hamed Jalali Gjergji Kasneci 67 4 0 17 Oct 2020
Stochastic embeddings of dynamical phenomena through variational autoencoders C. A. García P. Félix J. Presedo A. Otero BDL 58 2 0 13 Oct 2020
Recyclable Gaussian Processes P. Moreno-Muñoz Antonio Artés-Rodríguez Mauricio A. Alvarez BDL 47 1 0 06 Oct 2020
Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models Paul Westermann R. Evins AI4CE 46 44 0 05 Oct 2020
Gaussian Process Molecule Property Prediction with FlowMO Henry B. Moss Ryan-Rhys Griffiths 125 23 0 02 Oct 2020
Stein Variational Gaussian Processes Thomas Pinder Christopher Nemeth David Leslie BDL 61 7 0 25 Sep 2020
Fixed Inducing Points Online Bayesian Calibration for Computer Models with an Application to a Scale-Resolving CFD Simulation Y. Duan M. Eaton Michael Bluck 13 4 0 15 Sep 2020
Generalized Multi-Output Gaussian Process Censored Regression Daniele Gammelli Kasper Pryds Rolsted Dario Pacino Filipe Rodrigues 43 14 0 10 Sep 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 30 5 0 09 Sep 2020
Modulating Scalable Gaussian Processes for Expressive Statistical Learning Haitao Liu Yew-Soon Ong Xiaomo Jiang Xiaofang Wang 51 4 0 29 Aug 2020
Locally induced Gaussian processes for large-scale simulation experiments D. Cole R. Christianson R. Gramacy 77 21 0 28 Aug 2020
Fast Approximate Multi-output Gaussian Processes V. Joukov Dana Kulic 25 7 0 22 Aug 2020
Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling Nishant Yadav S. Ravela A. Ganguly OOD AI4Cl AI4CE 67 3 0 12 Aug 2020
Multifidelity Data Fusion via Gradient-Enhanced Gaussian Process Regression Yixiang Deng Guang Lin Xiu Yang 21 8 0 03 Aug 2020
Convergence of Sparse Variational Inference in Gaussian Processes Regression David R. Burt C. Rasmussen Mark van der Wilk 87 74 0 01 Aug 2020
On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice Li Yang Abdallah Shami AI4CE 218 2,158 0 30 Jul 2020
Multioutput Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard Assessment A. F. López-Lopera D. Idier J. Rohmer François Bachoc 67 22 0 28 Jul 2020
Probabilistic Active Meta-Learning Jean Kaddour Steindór Sæmundsson M. Deisenroth 98 35 0 17 Jul 2020
Orthogonally Decoupled Variational Fourier Features Dario Azzimonti Manuel Schürch A. Benavoli Marco Zaffalon 20 0 0 13 Jul 2020
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes William J. Wilkinson Paul E. Chang Michael Riis Andersen Arno Solin 58 13 0 12 Jul 2020
Bayesian Deep Ensembles via the Neural Tangent Kernel Bobby He Balaji Lakshminarayanan Yee Whye Teh BDL UQCV 84 121 0 11 Jul 2020
A Perspective on Gaussian Processes for Earth Observation Gustau Camps-Valls Dino Sejdinovic Jakob Runge Markus Reichstein GP 50 58 0 02 Jul 2020
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search Linnan Wang Rodrigo Fonseca Yuandong Tian 113 128 0 01 Jul 2020
Sparse Gaussian Processes with Spherical Harmonic Features Vincent Dutordoir N. Durrande J. Hensman 79 56 0 30 Jun 2020
Multi-fidelity modeling with different input domain definitions using Deep Gaussian Processes Ali Hebbal Loïc Brevault M. Balesdent El-Ghazali Talbi N. Melab AI4CE 68 36 0 29 Jun 2020
Stochastic Differential Equations with Variational Wishart Diffusions Martin Jørgensen M. Deisenroth Hugh Salimbeni DiffM 61 8 0 26 Jun 2020
Variational Orthogonal Features David R. Burt C. Rasmussen Mark van der Wilk BDL DRL 71 12 0 23 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 88 43 0 19 Jun 2020
Kernel methods through the roof: handling billions of points efficiently Giacomo Meanti Luigi Carratino Lorenzo Rosasco Alessandro Rudi 96 116 0 18 Jun 2020
Matérn Gaussian processes on Riemannian manifolds Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 134 123 0 17 Jun 2020
Longitudinal Variational Autoencoder S. Ramchandran Gleb Tikhonov Kalle Kujanpää Miika Koskinen Harri Lähdesmäki DRL CML BDL 88 37 0 17 Jun 2020
Balance is key: Private median splits yield high-utility random trees Shorya Consul Sinead Williamson 75 2 0 15 Jun 2020
Depth Uncertainty in Neural Networks Javier Antorán J. Allingham José Miguel Hernández-Lobato UQCV OOD BDL 119 103 0 15 Jun 2020
Approximate Inference for Spectral Mixture Kernel Yohan Jung Kyungwoo Song Jinkyoo Park BDL 23 2 0 12 Jun 2020
Fast increased fidelity approximate Gibbs samplers for Bayesian Gaussian process regression Kelly R. Moran M. Wheeler 43 4 0 11 Jun 2020