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1506.04132
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Stochastic Expectation Propagation
12 June 2015
Yingzhen Li
Jose Miguel Hernandez-Lobato
Richard Turner
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Papers citing
"Stochastic Expectation Propagation"
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Title
Fearless Stochasticity in Expectation Propagation
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Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling
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Yujian Liu
Kaizhi Qian
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Shiyu Chang
Yang Zhang
UD
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PER
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Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
Rui Li
S. T. John
Arno Solin
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56
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Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
Han Guo
P. Greengard
Hongyi Wang
Andrew Gelman
Yoon Kim
Eric P. Xing
FedML
75
21
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Variational Inference on the Final-Layer Output of Neural Networks
Yadi Wei
Roni Khardon
BDL
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91
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05 Feb 2023
Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning
Wenxuan Ma
Xing Yan
Kun Zhang
UQCV
66
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0
24 Dec 2022
GFlowNets and variational inference
Nikolay Malkin
Salem Lahlou
T. Deleu
Xu Ji
J. E. Hu
Katie Everett
Dinghuai Zhang
Yoshua Bengio
BDL
231
89
0
02 Oct 2022
Differentially private partitioned variational inference
Mikko A. Heikkilä
Matthew Ashman
S. Swaroop
Richard Turner
Antti Honkela
FedML
63
2
0
23 Sep 2022
Image Reconstruction by Splitting Expectation Propagation Techniques from Iterative Inversion
R. Aykroyd
Kehinde Olobatuyi
18
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0
25 Aug 2022
Task Agnostic and Post-hoc Unseen Distribution Detection
Radhika Dua
Seong-sil Yang
Yixuan Li
Edward Choi
OODD
66
11
0
26 Jul 2022
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Matthew Ashman
T. Bui
Cuong V Nguyen
Efstratios Markou
Adrian Weller
S. Swaroop
Richard Turner
FedML
65
14
0
24 Feb 2022
Approximate Inference via Clustering
Qianqian Song
66
0
0
28 Nov 2021
Differentially private stochastic expectation propagation (DP-SEP)
Margarita Vinaroz
Mijung Park
70
1
0
25 Nov 2021
Dual Parameterization of Sparse Variational Gaussian Processes
Vincent Adam
Paul E. Chang
Mohammad Emtiyaz Khan
Arno Solin
93
23
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05 Nov 2021
Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees
William J. Wilkinson
Simo Särkkä
Arno Solin
BDL
98
16
0
02 Nov 2021
Post-hoc loss-calibration for Bayesian neural networks
Meet P. Vadera
S. Ghosh
Kenney Ng
Benjamin M. Marlin
UQCV
BDL
88
7
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13 Jun 2021
Sparse Algorithms for Markovian Gaussian Processes
William J. Wilkinson
Arno Solin
Vincent Adam
62
12
0
19 Mar 2021
Sampling-free Variational Inference for Neural Networks with Multiplicative Activation Noise
Jannik Schmitt
Stefan Roth
UQCV
57
6
0
15 Mar 2021
f-Divergence Variational Inference
Neng Wan
Dapeng Li
N. Hovakimyan
114
35
0
28 Sep 2020
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
Lingkai Kong
Jimeng Sun
Chao Zhang
UQCV
107
108
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24 Aug 2020
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Belief Propagation for Approximate Inference
Dong Liu
Minh Thành Vu
Zuxing Li
L. Rasmussen
45
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27 Jun 2020
Infinite-dimensional gradient-based descent for alpha-divergence minimisation
Kamélia Daudel
Randal Douc
Franccois Portier
89
18
0
20 May 2020
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
Rui Zhang
Christian J. Walder
Edwin V. Bonilla
Marian-Andrei Rizoiu
Lexing Xie
48
2
0
21 Dec 2019
Conditional Expectation Propagation
Zheng Wang
Shandian Zhe
37
11
0
27 Oct 2019
Parametric Gaussian Process Regressors
M. Jankowiak
Geoffrey Pleiss
Jacob R. Gardner
UQCV
56
5
0
16 Oct 2019
The
f
f
f
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Kamélia Daudel
Randal Douc
Franccois Portier
François Roueff
97
1
0
26 Sep 2019
Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods
Haitao Liu
Yew-Soon Ong
Ziwei Yu
Jianfei Cai
Xiaobo Shen
66
3
0
14 Sep 2019
On the Expressiveness of Approximate Inference in Bayesian Neural Networks
Andrew Y. K. Foong
David R. Burt
Yingzhen Li
Richard Turner
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BDL
73
20
0
02 Sep 2019
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α
Belief Propagation as Fully Factorized Approximation
Dong Liu
N. N. Moghadam
L. Rasmussen
Jinliang Huang
Saikat Chatterjee
61
3
0
23 Aug 2019
Certainty Driven Consistency Loss on Multi-Teacher Networks for Semi-Supervised Learning
Lu Liu
R. Tan
82
32
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17 Jan 2019
Adversarial Learning of a Sampler Based on an Unnormalized Distribution
Chunyuan Li
Ke Bai
Jianqiao Li
Guoyin Wang
Changyou Chen
Lawrence Carin
155
10
0
03 Jan 2019
Uncertainty propagation in neural networks for sparse coding
Danil Kuzin
Olga Isupova
Lyudmila Mihaylova
BDL
UQCV
34
0
0
29 Nov 2018
Partitioned Variational Inference: A unified framework encompassing federated and continual learning
T. Bui
Cuong V Nguyen
S. Swaroop
Richard Turner
FedML
91
56
0
27 Nov 2018
Stochastic Particle-Optimization Sampling and the Non-Asymptotic Convergence Theory
Jianyi Zhang
Ruiyi Zhang
Lawrence Carin
Changyou Chen
146
46
0
05 Sep 2018
Variational Implicit Processes
Chao Ma
Yingzhen Li
José Miguel Hernández-Lobato
BDL
121
70
0
06 Jun 2018
Progress & Compress: A scalable framework for continual learning
Jonathan Richard Schwarz
Jelena Luketina
Wojciech M. Czarnecki
A. Grabska-Barwinska
Yee Whye Teh
Razvan Pascanu
R. Hadsell
CLL
161
889
0
16 May 2018
Graphical Generative Adversarial Networks
Chongxuan Li
Max Welling
Jun Zhu
Bo Zhang
GAN
47
36
0
10 Apr 2018
Learning Structural Weight Uncertainty for Sequential Decision-Making
Ruiyi Zhang
Chunyuan Li
Changyou Chen
Lawrence Carin
BDL
UQCV
101
26
0
30 Dec 2017
Advances in Variational Inference
Cheng Zhang
Judith Butepage
Hedvig Kjellström
Stephan Mandt
BDL
236
698
0
15 Nov 2017
Scalable Multi-Class Gaussian Process Classification using Expectation Propagation
Carlos Villacampa-Calvo
Daniel Hernández-Lobato
78
19
0
22 Jun 2017
Expectation Propagation for t-Exponential Family Using Q-Algebra
Futoshi Futami
Issei Sato
Masashi Sugiyama
68
6
0
25 May 2017
Dropout Inference in Bayesian Neural Networks with Alpha-divergences
Yingzhen Li
Y. Gal
UQCV
BDL
147
197
0
08 Mar 2017
Linear Time Computation of Moments in Sum-Product Networks
Haiying Zhao
Geoffrey J. Gordon
TPM
37
1
0
15 Feb 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
978
5,861
0
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On numerical approximation schemes for expectation propagation
A. Roche
29
0
0
14 Nov 2016
Variational Inference via
χ
χ
χ
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Adji Bousso Dieng
Dustin Tran
Rajesh Ranganath
John Paisley
David M. Blei
BDL
146
35
0
01 Nov 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Qiang Liu
Dilin Wang
BDL
142
1,096
0
16 Aug 2016
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation
T. Bui
Josiah Yan
Richard Turner
98
25
0
23 May 2016
Patterns of Scalable Bayesian Inference
E. Angelino
Matthew J. Johnson
Ryan P. Adams
107
87
0
16 Feb 2016
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
T. Bui
Daniel Hernández-Lobato
Yingzhen Li
José Miguel Hernández-Lobato
Richard Turner
BDL
158
237
0
12 Feb 2016
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