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Kernel-Based Just-In-Time Learning for Passing Expectation Propagation
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v1v2 (latest)

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages

9 March 2015
Wittawat Jitkrittum
Arthur Gretton
N. Heess
S. M. Ali Eslami
Balaji Lakshminarayanan
Dino Sejdinovic
Z. Szabó
ArXiv (abs)PDFHTML

Papers citing "Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages"

17 / 17 papers shown
Fearless Stochasticity in Expectation Propagation
Fearless Stochasticity in Expectation Propagation
Jonathan So
Richard Turner
209
0
0
03 Jun 2024
Importance Weighting Approach in Kernel Bayes' Rule
Importance Weighting Approach in Kernel Bayes' Rule
Liyuan Xu
Yutian Chen
Arnaud Doucet
Arthur Gretton
361
2
0
05 Feb 2022
Generalization of graph network inferences in higher-order graphical
  models
Generalization of graph network inferences in higher-order graphical models
Yicheng Fei
Xaq Pitkow
235
0
0
12 Jul 2021
Meta-Learning an Inference Algorithm for Probabilistic Programs
Meta-Learning an Inference Algorithm for Probabilistic Programs
Gwonsoo Che
Hongseok Yang
TPM
263
1
0
01 Mar 2021
Region-based Energy Neural Network for Approximate Inference
Region-based Energy Neural Network for Approximate Inference
Dong Liu
Ragnar Thobaben
L. Rasmussen
BDL
127
2
0
17 Jun 2020
Conditional Expectation Propagation
Conditional Expectation PropagationConference on Uncertainty in Artificial Intelligence (UAI), 2019
Zheng Wang
Shandian Zhe
153
12
0
27 Oct 2019
A Factor Graph Approach to Automated Design of Bayesian Signal
  Processing Algorithms
A Factor Graph Approach to Automated Design of Bayesian Signal Processing AlgorithmsInternational Journal of Approximate Reasoning (IJAR), 2018
Gautam Srivastava
T. V. D. Laar
Rajani Singh
122
56
0
08 Nov 2018
Domain Generalization by Marginal Transfer Learning
Domain Generalization by Marginal Transfer Learning
Gilles Blanchard
A. Deshmukh
Ürün Dogan
Gyemin Lee
Clayton Scott
OOD
316
329
0
21 Nov 2017
Bayesian Approaches to Distribution Regression
Bayesian Approaches to Distribution Regression
H. Law
Danica J. Sutherland
Dino Sejdinovic
Seth Flaxman
OODUQCVBDL
280
37
0
11 May 2017
Uncertain programming model for multi-item solid transportation problem
Uncertain programming model for multi-item solid transportation problemInternational Journal of Machine Learning and Cybernetics (IJMLC), 2016
Hasan Dalman
462
824
0
31 May 2016
Discriminative Embeddings of Latent Variable Models for Structured Data
Discriminative Embeddings of Latent Variable Models for Structured Data
H. Dai
Bo Dai
Le Song
BDL
491
745
0
17 Mar 2016
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution
  Regression
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
Jovana Mitrović
Dino Sejdinovic
Yee Whye Teh
BDL
142
33
0
15 Feb 2016
Distributed Bayesian Learning with Stochastic Natural-gradient
  Expectation Propagation and the Posterior Server
Distributed Bayesian Learning with Stochastic Natural-gradient Expectation Propagation and the Posterior Server
Leonard Hasenclever
Stefan Webb
Thibaut Lienart
Sebastian J. Vollmer
Balaji Lakshminarayanan
Charles Blundell
Yee Whye Teh
BDL
441
73
0
31 Dec 2015
Deep Mean Maps
Deep Mean Maps
Junier B. Oliva
Danica J. Sutherland
Barnabás Póczós
J. Schneider
179
8
0
13 Nov 2015
Linear-time Learning on Distributions with Approximate Kernel Embeddings
Linear-time Learning on Distributions with Approximate Kernel Embeddings
Danica J. Sutherland
Junier B. Oliva
Barnabás Póczós
J. Schneider
187
18
0
24 Sep 2015
Stochastic Expectation Propagation
Stochastic Expectation PropagationNeural Information Processing Systems (NeurIPS), 2015
Yingzhen Li
Jose Miguel Hernandez-Lobato
Richard Turner
310
121
0
12 Jun 2015
Mondrian Forests for Large-Scale Regression when Uncertainty Matters
Mondrian Forests for Large-Scale Regression when Uncertainty MattersInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2015
Balaji Lakshminarayanan
Daniel M. Roy
Yee Whye Teh
UQCV
322
59
0
11 Jun 2015
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