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Predictive variational inference: Learn the predictively optimal posterior distribution
v1v2 (latest)

Predictive variational inference: Learn the predictively optimal posterior distribution

18 October 2024
Jinlin Lai
Yuling Yao
    BDL
ArXiv (abs)PDFHTML

Papers citing "Predictive variational inference: Learn the predictively optimal posterior distribution"

24 / 24 papers shown
posteriordb: Testing, Benchmarking and Developing Bayesian Inference
  Algorithms
posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms
Måns Magnusson
Jakob Torgander
Paul-Christian Bürkner
Lu Zhang
Bob Carpenter
Aki Vehtari
260
15
0
06 Jul 2024
Simulation-based stacking
Simulation-based stackingInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Yuling Yao
Bruno Régaldo-Saint Blancard
Justin Domke
270
6
0
25 Oct 2023
Investigating the Impact of Model Misspecification in Neural
  Simulation-based Inference
Investigating the Impact of Model Misspecification in Neural Simulation-based Inference
Patrick W Cannon
Daniel Ward
Sebastian M. Schmon
213
45
0
05 Sep 2022
Robust Generalised Bayesian Inference for Intractable Likelihoods
Robust Generalised Bayesian Inference for Intractable Likelihoods
Takuo Matsubara
Jeremias Knoblauch
François‐Xavier Briol
Chris J. Oates
UQCV
309
98
0
15 Apr 2021
Martingale posterior distributions
Martingale posterior distributions
Edwin Fong
Chris Holmes
S. Walker
UQCV
366
75
0
29 Mar 2021
Neural Empirical Bayes: Source Distribution Estimation and its
  Applications to Simulation-Based Inference
Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference
M. Vandegar
Michael Kagan
Antoine Wehenkel
Gilles Louppe
220
33
0
11 Nov 2020
Reproducible Model Selection Using Bagged Posteriors
Reproducible Model Selection Using Bagged PosteriorsBayesian Analysis (BA), 2020
Jonathan H. Huggins
Jeffrey W. Miller
198
17
0
24 Jul 2020
Holes in Bayesian Statistics
Holes in Bayesian StatisticsJournal of Physics G: Nuclear and Particle Physics (J. Phys. G), 2020
Andrew Gelman
Yuling Yao
118
27
0
15 Feb 2020
Normalizing Flows for Probabilistic Modeling and Inference
Normalizing Flows for Probabilistic Modeling and InferenceJournal of machine learning research (JMLR), 2019
George Papamakarios
Eric T. Nalisnick
Danilo Jimenez Rezende
S. Mohamed
Balaji Lakshminarayanan
TPMAI4CE
713
2,061
0
05 Dec 2019
The frontier of simulation-based inference
The frontier of simulation-based inferenceProceedings of the National Academy of Sciences of the United States of America (PNAS), 2019
Kyle Cranmer
Johann Brehmer
Gilles Louppe
AI4CE
638
1,053
0
04 Nov 2019
Neural Spline Flows
Neural Spline FlowsNeural Information Processing Systems (NeurIPS), 2019
Conor Durkan
Artur Bekasov
Iain Murray
George Papamakarios
DRL
988
892
0
10 Jun 2019
Variational Bayes under Model Misspecification
Variational Bayes under Model MisspecificationNeural Information Processing Systems (NeurIPS), 2019
Yixin Wang
David M. Blei
200
51
0
26 May 2019
Variational Bayesian Decision-making for Continuous Utilities
Variational Bayesian Decision-making for Continuous UtilitiesNeural Information Processing Systems (NeurIPS), 2019
Tomasz Kuśmierczyk
J. Sakaya
Arto Klami
355
21
0
02 Feb 2019
Adversarial Variational Optimization of Non-Differentiable Simulators
Adversarial Variational Optimization of Non-Differentiable Simulators
Gilles Louppe
Joeri Hermans
Kyle Cranmer
GAN
404
69
0
22 Jul 2017
A Bayes interpretation of stacking for M-complete and M-open settings
A Bayes interpretation of stacking for M-complete and M-open settings
Tri Le
B. Clarke
239
43
0
16 Feb 2016
Variational Inference: A Review for Statisticians
Variational Inference: A Review for Statisticians
David M. Blei
A. Kucukelbir
Jon D. McAuliffe
BDL
1.7K
5,306
0
04 Jan 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
3.7K
217,209
0
10 Dec 2015
A General Method for Robust Bayesian Modeling
A General Method for Robust Bayesian Modeling
Chong-Jun Wang
David M. Blei
OOD
286
63
0
17 Oct 2015
On the properties of variational approximations of Gibbs posteriors
On the properties of variational approximations of Gibbs posteriorsJournal of machine learning research (JMLR), 2015
Pierre Alquier
James Ridgway
Nicolas Chopin
343
273
0
12 Jun 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic OptimizationInternational Conference on Learning Representations (ICLR), 2014
Diederik P. Kingma
Jimmy Ba
ODL
4.7K
161,471
0
22 Dec 2014
Inconsistency of Bayesian Inference for Misspecified Linear Models, and
  a Proposal for Repairing It
Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It
Peter Grünwald
T. V. Ommen
445
284
0
11 Dec 2014
A General Framework for Updating Belief Distributions
A General Framework for Updating Belief DistributionsJournal of The Royal Statistical Society Series B-statistical Methodology (JRSSB), 2013
Pier Giovanni Bissiri
Chris Holmes
S. Walker
497
542
0
27 Jun 2013
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian
  Monte Carlo
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte CarloJournal of machine learning research (JMLR), 2011
Matthew D. Hoffman
Andrew Gelman
474
4,771
0
18 Nov 2011
Gibbs posterior for variable selection in high-dimensional
  classification and data mining
Gibbs posterior for variable selection in high-dimensional classification and data mining
Wenxin Jiang
M. Tanner
213
123
0
31 Oct 2008
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