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Stochastic Expectation Propagation
v1v2 (latest)

Stochastic Expectation Propagation

12 June 2015
Yingzhen Li
Jose Miguel Hernandez-Lobato
Richard Turner
ArXiv (abs)PDFHTML

Papers citing "Stochastic Expectation Propagation"

50 / 59 papers shown
Title
Fearless Stochasticity in Expectation Propagation
Fearless Stochasticity in Expectation Propagation
Jonathan So
Richard Turner
68
0
0
03 Jun 2024
Decomposing Uncertainty for Large Language Models through Input
  Clarification Ensembling
Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling
Bairu Hou
Yujian Liu
Kaizhi Qian
Jacob Andreas
Shiyu Chang
Yang Zhang
UDUQCVPER
98
65
0
15 Nov 2023
Improving Hyperparameter Learning under Approximate Inference in
  Gaussian Process Models
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models
Rui Li
S. T. John
Arno Solin
BDL
56
3
0
07 Jun 2023
Federated Learning as Variational Inference: A Scalable Expectation
  Propagation Approach
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
0
08 Feb 2023
Variational Inference on the Final-Layer Output of Neural Networks
Variational Inference on the Final-Layer Output of Neural Networks
Yadi Wei
Roni Khardon
BDLUQCV
91
0
0
05 Feb 2023
Improving Uncertainty Quantification of Variance Networks by
  Tree-Structured Learning
Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning
Wenxuan Ma
Xing Yan
Kun Zhang
UQCV
66
0
0
24 Dec 2022
GFlowNets and variational inference
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
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
Image Reconstruction by Splitting Expectation Propagation Techniques from Iterative Inversion
R. Aykroyd
Kehinde Olobatuyi
18
0
0
25 Aug 2022
Task Agnostic and Post-hoc Unseen Distribution Detection
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
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
Approximate Inference via Clustering
Qianqian Song
66
0
0
28 Nov 2021
Differentially private stochastic expectation propagation (DP-SEP)
Differentially private stochastic expectation propagation (DP-SEP)
Margarita Vinaroz
Mijung Park
70
1
0
25 Nov 2021
Dual Parameterization of Sparse Variational Gaussian Processes
Dual Parameterization of Sparse Variational Gaussian Processes
Vincent Adam
Paul E. Chang
Mohammad Emtiyaz Khan
Arno Solin
93
23
0
05 Nov 2021
Bayes-Newton Methods for Approximate Bayesian Inference with PSD
  Guarantees
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
Post-hoc loss-calibration for Bayesian neural networks
Meet P. Vadera
S. Ghosh
Kenney Ng
Benjamin M. Marlin
UQCVBDL
88
7
0
13 Jun 2021
Sparse Algorithms for Markovian Gaussian Processes
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
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
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
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
Lingkai Kong
Jimeng Sun
Chao Zhang
UQCV
107
108
0
24 Aug 2020
$α$ Belief Propagation for Approximate Inference
ααα Belief Propagation for Approximate Inference
Dong Liu
Minh Thành Vu
Zuxing Li
L. Rasmussen
39
0
0
27 Jun 2020
Infinite-dimensional gradient-based descent for alpha-divergence
  minimisation
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
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
Rui Zhang
Christian J. Walder
Edwin V. Bonilla
Marian-Andrei Rizoiu
Lexing Xie
34
2
0
21 Dec 2019
Conditional Expectation Propagation
Conditional Expectation Propagation
Zheng Wang
Shandian Zhe
37
11
0
27 Oct 2019
Parametric Gaussian Process Regressors
Parametric Gaussian Process Regressors
M. Jankowiak
Geoffrey Pleiss
Jacob R. Gardner
UQCV
56
5
0
16 Oct 2019
The fff-Divergence Expectation Iteration Scheme
Kamélia Daudel
Randal Douc
Franccois Portier
François Roueff
97
1
0
26 Sep 2019
Scalable Gaussian Process Classification with Additive Noise for Various
  Likelihoods
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
On the Expressiveness of Approximate Inference in Bayesian Neural Networks
Andrew Y. K. Foong
David R. Burt
Yingzhen Li
Richard Turner
UQCVBDL
73
20
0
02 Sep 2019
$α$ Belief Propagation as Fully Factorized Approximation
ααα 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
Certainty Driven Consistency Loss on Multi-Teacher Networks for Semi-Supervised Learning
Lu Liu
R. Tan
82
32
0
17 Jan 2019
Adversarial Learning of a Sampler Based on an Unnormalized Distribution
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
Uncertainty propagation in neural networks for sparse coding
Danil Kuzin
Olga Isupova
Lyudmila Mihaylova
BDLUQCV
34
0
0
29 Nov 2018
Partitioned Variational Inference: A unified framework encompassing
  federated and continual learning
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
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
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
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
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
Learning Structural Weight Uncertainty for Sequential Decision-Making
Ruiyi Zhang
Chunyuan Li
Changyou Chen
Lawrence Carin
BDLUQCV
101
26
0
30 Dec 2017
Advances in Variational Inference
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
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
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
Dropout Inference in Bayesian Neural Networks with Alpha-divergences
Yingzhen Li
Y. Gal
UQCVBDL
147
197
0
08 Mar 2017
Linear Time Computation of Moments in Sum-Product Networks
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
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCVBDL
972
5,861
0
05 Dec 2016
On numerical approximation schemes for expectation propagation
On numerical approximation schemes for expectation propagation
A. Roche
29
0
0
14 Nov 2016
Variational Inference via $χ$-Upper Bound Minimization
Variational Inference via χχχ-Upper Bound Minimization
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
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
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
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
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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