Scalable Variational Gaussian Process Classification

7 November 2014

Papers citing "Scalable Variational Gaussian Process Classification"

37 / 337 papers shown

Title
Variational zero-inflated Gaussian processes with sparse kernels Pashupati Hegde Markus Heinonen Samuel Kaski 26 5 0 13 Mar 2018
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling C. Riquelme George Tucker Jasper Snoek BDL 93 366 0 26 Feb 2018
Conditionally Independent Multiresolution Gaussian Processes Jalil Taghia Thomas B. Schon 64 1 0 25 Feb 2018
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation F. Wenzel Théo Galy-Fajou Christian Donner Marius Kloft Manfred Opper 87 36 0 18 Feb 2018
Few-shot learning of neural networks from scratch by pseudo example optimization Akisato Kimura Zoubin Ghahramani Koh Takeuchi Tomoharu Iwata N. Ueda 90 52 0 08 Feb 2018
Variational Inference for Gaussian Process Models with Linear Complexity Ching-An Cheng Byron Boots BDL 63 75 0 28 Nov 2017
Fidelity-Weighted Learning Mostafa Dehghani Arash Mehrjou Stephan Gouws J. Kamps Bernhard Schölkopf NoLa FedML 96 75 0 08 Nov 2017
Structured Variational Inference for Coupled Gaussian Processes Vincent Adam 50 2 0 03 Nov 2017
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition Pavel Izmailov Alexander Novikov D. Kropotov 87 62 0 19 Oct 2017
Bayesian Alignments of Warped Multi-Output Gaussian Processes Markus Kaiser Clemens Otte Thomas Runkler Carl Henrik Ek 62 19 0 08 Oct 2017
Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy Jens B. Stephansen A. N. Olesen Mads Olsen A. Ambati E. Leary ... C. Kushida P. Peppard H. Sørensen P. Jennum Emmanuel Mignot 88 228 0 05 Oct 2017
Convolutional Gaussian Processes Mark van der Wilk C. Rasmussen J. Hensman BDL 85 132 0 06 Sep 2017
Pillar Networks++: Distributed non-parametric deep and wide networks B. Sengupta Y. Qian 29 4 0 18 Aug 2017
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction Hossein Soleimani J. Hensman Suchi Saria 73 60 0 16 Aug 2017
Bayesian Nonlinear Support Vector Machines for Big Data F. Wenzel Théo Galy-Fajou M. Deutsch Marius Kloft BDL 65 27 0 18 Jul 2017
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks John Bradshaw A. G. Matthews Zoubin Ghahramani BDL AAML 117 172 0 08 Jul 2017
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation Carlos Villacampa-Calvo Daniel Hernández-Lobato 70 19 0 22 Jun 2017
Streaming Sparse Gaussian Process Approximations T. Bui Cuong V Nguyen Richard Turner 76 103 0 19 May 2017
Parametric Gaussian Process Regression for Big Data M. Raissi 91 39 0 11 Apr 2017
Treatment-Response Models for Counterfactual Reasoning with Continuous-time, Continuous-valued Interventions Hossein Soleimani Adarsh Subbaswamy Suchi Saria CML 73 23 0 06 Apr 2017
Occupancy Map Building through Bayesian Exploration Gilad Francis Lionel Ott Román Marchant F. Ramos 81 22 0 01 Mar 2017
Variational Fourier features for Gaussian processes J. Hensman N. Durrande Arno Solin VLM 93 202 0 21 Nov 2016
Faster variational inducing input Gaussian process classification Pavel Izmailov D. Kropotov 30 2 0 18 Nov 2016
Stochastic Variational Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric Xing BDL 131 267 0 01 Nov 2016
GPflow: A Gaussian process library using TensorFlow A. G. Matthews Mark van der Wilk T. Nickson Keisuke Fujii A. Boukouvalas Pablo León-Villagrá Zoubin Ghahramani J. Hensman GP 89 665 0 27 Oct 2016
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models K. Krauth Edwin V. Bonilla Kurt Cutajar Maurizio Filippone GP BDL 61 54 0 18 Oct 2016
Random Feature Expansions for Deep Gaussian Processes Kurt Cutajar Edwin V. Bonilla Pietro Michiardi Maurizio Filippone BDL 58 144 0 14 Oct 2016
Gray-box inference for structured Gaussian process models P. Galliani Amir Dezfouli Edwin V. Bonilla Novi Quadrianto BDL 23 4 0 14 Sep 2016
Generic Inference in Latent Gaussian Process Models Edwin V. Bonilla K. Krauth Amir Dezfouli BDL 64 28 0 02 Sep 2016
Understanding Probabilistic Sparse Gaussian Process Approximations Matthias Bauer Mark van der Wilk C. Rasmussen 72 259 0 15 Jun 2016
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation T. Bui Josiah Yan Richard Turner 89 25 0 23 May 2016
Chained Gaussian Processes Alan D. Saul J. Hensman Aki Vehtari Neil D. Lawrence 47 59 0 18 Apr 2016
Stochastic Expectation Propagation for Large Scale Gaussian Process Classification Daniel Hernández-Lobato José Miguel Hernández-Lobato Yingzhen Li T. Bui Richard Turner BDL 34 0 0 10 Nov 2015
String and Membrane Gaussian Processes Yves-Laurent Kom Samo Stephen J. Roberts 85 18 0 24 Jul 2015
Scalable Gaussian Process Classification via Expectation Propagation Daniel Hernández-Lobato José Miguel Hernández-Lobato 77 52 0 16 Jul 2015
MCMC for Variationally Sparse Gaussian Processes J. Hensman A. G. Matthews Maurizio Filippone Zoubin Ghahramani 83 141 0 12 Jun 2015
On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes A. G. Matthews J. Hensman Richard Turner Zoubin Ghahramani 115 192 0 27 Apr 2015