Scalable Variational Gaussian Process Classification

7 November 2014

Papers citing "Scalable Variational Gaussian Process Classification"

50 / 337 papers shown

Title
Improving predictions of Bayesian neural nets via local linearization Alexander Immer M. Korzepa Matthias Bauer BDL 80 11 0 19 Aug 2020
A statistical theory of cold posteriors in deep neural networks Laurence Aitchison UQCV BDL 80 70 0 13 Aug 2020
Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships N. Kudryashova Theoklitos Amvrosiadis Nathalie Dupuy Nathalie L Rochefort A. Onken 60 5 0 03 Aug 2020
Convergence of Sparse Variational Inference in Gaussian Processes Regression David R. Burt C. Rasmussen Mark van der Wilk 82 74 0 01 Aug 2020
Towards Credit-Fraud Detection via Sparsely Varying Gaussian Approximations Harshit Sharma H. Gandhi Apoorv Jain 13 0 0 14 Jul 2020
Orthogonally Decoupled Variational Fourier Features Dario Azzimonti Manuel Schürch A. Benavoli Marco Zaffalon 17 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
Fast Variational Learning in State-Space Gaussian Process Models Paul E. Chang William J. Wilkinson Mohammad Emtiyaz Khan Arno Solin BDL 79 24 0 09 Jul 2020
Sparse Gaussian Processes with Spherical Harmonic Features Vincent Dutordoir N. Durrande J. Hensman 71 56 0 30 Jun 2020
Variational Autoencoding of PDE Inverse Problems Daniel J. Tait Theodoros Damoulas AI4CE 49 12 0 28 Jun 2020
Automatic Tuning of Stochastic Gradient Descent with Bayesian Optimisation Victor Picheny Vincent Dutordoir A. Artemev N. Durrande 43 2 0 25 Jun 2020
Variational Orthogonal Features David R. Burt C. Rasmussen Mark van der Wilk BDL DRL 65 12 0 23 Jun 2020
Towards Adaptive Benthic Habitat Mapping J. Shields Oscar Pizarro Stefan B. Williams 47 15 0 20 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 80 43 0 19 Jun 2020
Kernel methods through the roof: handling billions of points efficiently Giacomo Meanti Luigi Carratino Lorenzo Rosasco Alessandro Rudi 90 116 0 18 Jun 2020
GPIRT: A Gaussian Process Model for Item Response Theory JBrandon Duck-Mayr Roman Garnett Jacob Montgomery 23 8 0 17 Jun 2020
Gaussian Processes on Graphs via Spectral Kernel Learning Yin-Cong Zhi Yin Cheng Ng Xiaowen Dong 39 32 0 12 Jun 2020
Approximate Inference for Spectral Mixture Kernel Yohan Jung Kyungwoo Song Jinkyoo Park BDL 18 2 0 12 Jun 2020
Variational Auto-Regressive Gaussian Processes for Continual Learning Sanyam Kapoor Theofanis Karaletsos T. Bui BDL 75 26 0 09 Jun 2020
A Survey of Bayesian Statistical Approaches for Big Data Farzana Jahan Insha Ullah Kerrie Mengersen 102 14 0 08 Jun 2020
Learning Inconsistent Preferences with Gaussian Processes Siu Lun Chau Javier I. González Dino Sejdinovic 51 7 0 06 Jun 2020
Quadruply Stochastic Gaussian Processes Trefor W. Evans P. Nair GP 38 3 0 04 Jun 2020
Multi-Fidelity Black-Box Optimization for Time-Optimal Quadrotor Maneuvers Gilhyun Ryou E. Tal S. Karaman 80 42 0 03 Jun 2020
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes Sebastian W. Ober Laurence Aitchison BDL 110 60 0 17 May 2020
Direct loss minimization algorithms for sparse Gaussian processes Yadi Wei Rishit Sheth Roni Khardon 56 14 0 07 Apr 2020
How Good are Low-Rank Approximations in Gaussian Process Regression? C. Daskalakis P. Dellaportas A. Panos 60 3 0 03 Apr 2020
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations Simone Rossi Markus Heinonen Edwin V. Bonilla Zheyan Shen Maurizio Filippone UQCV BDL 42 0 0 06 Mar 2020
Knot Selection in Sparse Gaussian Processes with a Variational Objective Function Nathaniel Garton Jarad Niemi A. Carriquiry 27 2 0 05 Mar 2020
A Framework for Interdomain and Multioutput Gaussian Processes Mark van der Wilk Vincent Dutordoir S. T. John A. Artemev Vincent Adam J. Hensman 100 95 0 02 Mar 2020
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models Théo Galy-Fajou F. Wenzel Manfred Opper 54 4 0 26 Feb 2020
Knot Selection in Sparse Gaussian Processes Nathaniel Garton Jarad Niemi A. Carriquiry 18 4 0 21 Feb 2020
Bayesian task embedding for few-shot Bayesian optimization Steven Atkinson Sayan Ghosh Natarajan Chennimalai-Kumar Genghis Khan Liping Wang BDL 26 1 0 02 Jan 2020
Quantile Propagation for Wasserstein-Approximate Gaussian Processes Rui Zhang Christian J. Walder Edwin V. Bonilla Marian-Andrei Rizoiu Lexing Xie 8 2 0 21 Dec 2019
Scalable Bayesian Preference Learning for Crowds Edwin Simpson Iryna Gurevych BDL 99 24 0 04 Dec 2019
Implicit Priors for Knowledge Sharing in Bayesian Neural Networks Jack K. Fitzsimons Sebastian M. Schmon Stephen J. Roberts BDL FedML 30 0 0 02 Dec 2019
A Fully Natural Gradient Scheme for Improving Inference of the Heterogeneous Multi-Output Gaussian Process Model Juan J. Giraldo Mauricio A. Alvarez BDL 96 5 0 22 Nov 2019
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO Pablo Morales-Álvarez Pablo Ruiz S. Coughlin Rafael Molina Aggelos K. Katsaggelos 38 14 0 05 Nov 2019
Continual Multi-task Gaussian Processes P. Moreno-Muñoz A. Artés-Rodríguez Mauricio A. Alvarez 69 13 0 31 Oct 2019
Parametric Gaussian Process Regressors M. Jankowiak Geoffrey Pleiss Jacob R. Gardner UQCV 56 5 0 16 Oct 2019
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels Massimiliano Patacchiola Jack Turner Elliot J. Crowley Michael F. P. O'Boyle Amos Storkey BDL 74 19 0 11 Oct 2019
Adversarial Vulnerability Bounds for Gaussian Process Classification M. Smith Kathrin Grosse Michael Backes Mauricio A. Alvarez AAML 47 9 0 19 Sep 2019
Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods Haitao Liu Yew-Soon Ong Ziwei Yu Jianfei Cai Xiaobo Shen 50 3 0 14 Sep 2019
Latent Gaussian process with composite likelihoods and numerical quadrature S. Ramchandran Miika Koskinen Harri Lähdesmäki 22 0 0 04 Sep 2019
Kernel Mode Decomposition and programmable/interpretable regression networks H. Owhadi C. Scovel G. Yoo 95 5 0 19 Jul 2019
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning Markus Kaiser Clemens Otte Thomas Runkler Carl Henrik Ek 35 3 0 10 Jul 2019
An innovative adaptive kriging approach for efficient binary classification of mechanical problems J. Fuhg A. Fau AI4CE 29 2 0 02 Jul 2019
Multi-task Learning for Aggregated Data using Gaussian Processes F. Yousefi M. Smith Mauricio A. Alvarez FedML 53 34 0 22 Jun 2019
Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes Creighton Heaukulani Mark van der Wilk BDL 89 15 0 22 Jun 2019
Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances Csaba Tóth Harald Oberhauser 38 9 0 19 Jun 2019
A Survey of Optimization Methods from a Machine Learning Perspective Shiliang Sun Zehui Cao Han Zhu Jing Zhao 76 561 0 17 Jun 2019