Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales
Search
Open menu
Home
Papers
1507.04513
Cited By
Scalable Gaussian Process Classification via Expectation Propagation
International Conference on Artificial Intelligence and Statistics (AISTATS), 2015
16 July 2015
Daniel Hernández-Lobato
José Miguel Hernández-Lobato
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"Scalable Gaussian Process Classification via Expectation Propagation"
34 / 34 papers shown
Fearless Stochasticity in Expectation Propagation
Jonathan So
Richard Turner
264
0
0
03 Jun 2024
Function-Space Regularization for Deep Bayesian Classification
J. Lin
Joe Watson
Pascal Klink
Jan Peters
UQCV
BDL
238
1
0
12 Jul 2023
Learning Choice Functions with Gaussian Processes
Conference on Uncertainty in Artificial Intelligence (UAI), 2023
A. Benavoli
Dario Azzimonti
Dario Piga
176
7
0
01 Feb 2023
Gaussian Processes to speed up MCMC with automatic exploratory-exploitation effect
A. Benavoli
J. Wyse
Arthur J. White
GP
121
0
0
28 Sep 2021
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products
Social Science Research Network (SSRN), 2021
Maryam Habibpour
Hassan Gharoun
AmirReza Tajally
Afshar Shamsi Jokandan
Hamzeh Asgharnezhad
Abbas Khosravi
S. Nahavandi
UQCV
262
20
0
24 Jul 2021
Input Dependent Sparse Gaussian Processes
International Conference on Machine Learning (ICML), 2021
B. Jafrasteh
Carlos Villacampa-Calvo
Daniel Hernández-Lobato
UQCV
283
7
0
15 Jul 2021
Active and sparse methods in smoothed model checking
International Conference on Quantitative Evaluation of Systems (QEST), 2021
Paul Piho
J. Hillston
123
3
0
20 Apr 2021
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes
Machine-mediated learning (ML), 2020
A. Benavoli
Dario Azzimonti
Dario Piga
268
15
0
12 Dec 2020
Stein Variational Gaussian Processes
Thomas Pinder
Christopher Nemeth
David Leslie
BDL
351
7
0
25 Sep 2020
Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships
N. Kudryashova
Theoklitos Amvrosiadis
Nathalie Dupuy
Nathalie L Rochefort
A. Onken
204
7
0
03 Aug 2020
Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes
Jake C. Snell
R. Zemel
352
68
0
20 Jul 2020
Intrinsic Gaussian Processes on Manifolds and Their Accelerations by Symmetry
Ke Ye
Mu Niu
P. Cheung
Zhenwen Dai
Yuan Liu
297
2
0
25 Jun 2020
Knot Selection in Sparse Gaussian Processes with a Variational Objective Function
Statistical analysis and data mining (Stat. Anal. Data Min.), 2020
Nathaniel Garton
Jarad Niemi
A. Carriquiry
193
4
0
05 Mar 2020
Knot Selection in Sparse Gaussian Processes
Nathaniel Garton
Jarad Niemi
A. Carriquiry
144
4
0
21 Feb 2020
Deep Sigma Point Processes
Conference on Uncertainty in Artificial Intelligence (UAI), 2020
M. Jankowiak
Geoff Pleiss
Jacob R. Gardner
BDL
274
27
0
21 Feb 2020
Parametric Gaussian Process Regressors
M. Jankowiak
Geoffrey Pleiss
Jacob R. Gardner
UQCV
365
6
0
16 Oct 2019
Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods
Haitao Liu
Yew-Soon Ong
Ziwei Yu
Jianfei Cai
Xiaobo Shen
204
4
0
14 Sep 2019
Knowing The What But Not The Where in Bayesian Optimization
International Conference on Machine Learning (ICML), 2019
Vu Nguyen
Michael A. Osborne
469
42
0
07 May 2019
Fully Scalable Gaussian Processes using Subspace Inducing Inputs
A. Panos
P. Dellaportas
Michalis K. Titsias
210
12
0
06 Jul 2018
When Gaussian Process Meets Big Data: A Review of Scalable GPs
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2018
Haitao Liu
Yew-Soon Ong
Xiaobo Shen
Jianfei Cai
GP
559
841
0
03 Jul 2018
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
Dimitrios Milios
Raffaello Camoriano
Pietro Michiardi
Lorenzo Rosasco
Maurizio Filippone
UQCV
292
87
0
28 May 2018
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
F. Wenzel
Théo Galy-Fajou
Christian Donner
Matthias Kirchler
Manfred Opper
280
40
0
18 Feb 2018
Convolutional Gaussian Processes
Mark van der Wilk
C. Rasmussen
J. Hensman
BDL
308
136
0
06 Sep 2017
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation
Carlos Villacampa-Calvo
Daniel Hernández-Lobato
198
20
0
22 Jun 2017
Expectation Propagation for t-Exponential Family Using Q-Algebra
Futoshi Futami
Issei Sato
Masashi Sugiyama
188
6
0
25 May 2017
Streaming Sparse Gaussian Process Approximations
T. Bui
Cuong V Nguyen
Richard Turner
197
122
0
19 May 2017
Embarrassingly Parallel Inference for Gaussian Processes
Journal of machine learning research (JMLR), 2017
M. Zhang
Sinead Williamson
462
26
0
27 Feb 2017
Generic Inference in Latent Gaussian Process Models
Edwin V. Bonilla
K. Krauth
Amir Dezfouli
BDL
309
30
0
02 Sep 2016
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
T. Bui
Josiah Yan
Richard Turner
332
25
0
23 May 2016
Scalable Gaussian Processes for Supervised Hashing
B. Ozdemir
L. Davis
51
2
0
25 Apr 2016
Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation
T. Bui
José Miguel Hernández-Lobato
Yingzhen Li
Daniel Hernández-Lobato
Richard Turner
BDL
GP
178
8
0
11 Nov 2015
Stochastic Expectation Propagation for Large Scale Gaussian Process Classification
Daniel Hernández-Lobato
José Miguel Hernández-Lobato
Yingzhen Li
T. Bui
Richard Turner
BDL
145
0
0
10 Nov 2015
Black-box
α
α
α
-divergence Minimization
José Miguel Hernández-Lobato
Yingzhen Li
Mark Rowland
Daniel Hernández-Lobato
T. Bui
Richard Turner
377
148
0
10 Nov 2015
Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data
Journal of machine learning research (JMLR), 2014
Aki Vehtari
Andrew Gelman
Tuomas Sivula
Pasi Jylänki
Dustin Tran
Swupnil Sahai
Paul Blomstedt
John P. Cunningham
D. Schiminovich
Christian P. Robert
517
39
0
16 Dec 2014
1
Page 1 of 1
