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Variational Bayesian Monte Carlo with Noisy Likelihoods

Variational Bayesian Monte Carlo with Noisy Likelihoods

15 June 2020
Luigi Acerbi
ArXivPDFHTML

Papers citing "Variational Bayesian Monte Carlo with Noisy Likelihoods"

31 / 31 papers shown
Title
Bayesian Adaptive Calibration and Optimal Design
Bayesian Adaptive Calibration and Optimal Design
Rafael Oliveira
Dino Sejdinovic
David Howard
Edwin Bonilla
124
0
0
20 Jan 2025
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC
A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC
F. Llorente
Luca Martino
Jesse Read
D. Delgado
OffRL
118
13
0
03 Jan 2025
Bayesian Synthetic Likelihood
Bayesian Synthetic Likelihood
David T. Frazier
Christopher C. Drovandi
David J. Nott
75
217
0
09 May 2023
Unbiased and Efficient Log-Likelihood Estimation with Inverse Binomial
  Sampling
Unbiased and Efficient Log-Likelihood Estimation with Inverse Binomial Sampling
B. V. Opheusden
Luigi Acerbi
W. Ma
35
37
0
12 Jan 2020
Energy and Policy Considerations for Deep Learning in NLP
Energy and Policy Considerations for Deep Learning in NLP
Emma Strubell
Ananya Ganesh
Andrew McCallum
31
2,633
0
05 Jun 2019
Convergence Guarantees for Adaptive Bayesian Quadrature Methods
Convergence Guarantees for Adaptive Bayesian Quadrature Methods
Motonobu Kanagawa
Philipp Hennig
33
35
0
24 May 2019
Automatic Posterior Transformation for Likelihood-Free Inference
Automatic Posterior Transformation for Likelihood-Free Inference
David S. Greenberg
M. Nonnenmacher
Jakob H. Macke
106
319
0
17 May 2019
Parallel Gaussian process surrogate Bayesian inference with noisy
  likelihood evaluations
Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations
Marko Jarvenpaa
Michael U. Gutmann
Aki Vehtari
Pekka Marttinen
34
40
0
03 May 2019
Active Multi-Information Source Bayesian Quadrature
Active Multi-Information Source Bayesian Quadrature
A. Gessner
Javier I. González
Maren Mahsereci
31
30
0
27 Mar 2019
Automated Model Selection with Bayesian Quadrature
Automated Model Selection with Bayesian Quadrature
Henry Chai
Jean-François Ton
Roman Garnett
Michael A. Osborne
25
11
0
26 Feb 2019
Variational Bayesian Monte Carlo
Variational Bayesian Monte Carlo
Luigi Acerbi
BDL
36
64
0
12 Oct 2018
Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy
  Level Set Estimation
Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation
Xiong Lyu
M. Binois
M. Ludkovski
12
24
0
18 Jul 2018
Why Is My Classifier Discriminatory?
Why Is My Classifier Discriminatory?
Irene Y. Chen
Fredrik D. Johansson
David Sontag
FaML
42
395
0
30 May 2018
Yes, but Did It Work?: Evaluating Variational Inference
Yes, but Did It Work?: Evaluating Variational Inference
Yuling Yao
Aki Vehtari
Daniel P. Simpson
Andrew Gelman
24
136
0
07 Feb 2018
Flexible statistical inference for mechanistic models of neural dynamics
Flexible statistical inference for mechanistic models of neural dynamics
Jan-Matthis Lueckmann
P. J. Gonçalves
Giacomo Bassetto
Kaan Öcal
M. Nonnenmacher
Jakob H. Macke
78
241
0
06 Nov 2017
Constrained Bayesian Optimization with Noisy Experiments
Constrained Bayesian Optimization with Noisy Experiments
Benjamin Letham
Brian Karrer
Guilherme Ottoni
E. Bakshy
35
298
0
21 Jun 2017
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive
  Direct Search
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search
Luigi Acerbi
Wei Ji
36
218
0
11 May 2017
Efficient acquisition rules for model-based approximate Bayesian
  computation
Efficient acquisition rules for model-based approximate Bayesian computation
Marko Jarvenpaa
Michael U. Gutmann
Arijus Pleska
Aki Vehtari
Pekka Marttinen
TPM
65
69
0
03 Apr 2017
Adaptive Gaussian process approximation for Bayesian inference with
  expensive likelihood functions
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions
Hongqiao Wang
Jinglai Li
GP
33
61
0
29 Mar 2017
Variational Boosting: Iteratively Refining Posterior Approximations
Variational Boosting: Iteratively Refining Posterior Approximations
Andrew C. Miller
N. Foti
Ryan P. Adams
38
124
0
20 Nov 2016
Gaussian process modeling in approximate Bayesian computation to
  estimate horizontal gene transfer in bacteria
Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria
Marko Jarvenpaa
Michael U. Gutmann
Aki Vehtari
Pekka Marttinen
86
41
0
20 Oct 2016
Fast $ε$-free Inference of Simulation Models with Bayesian
  Conditional Density Estimation
Fast εεε-free Inference of Simulation Models with Bayesian Conditional Density Estimation
George Papamakarios
Iain Murray
TPM
76
158
0
20 May 2016
Variational Inference: A Review for Statisticians
Variational Inference: A Review for Statisticians
David M. Blei
A. Kucukelbir
Jon D. McAuliffe
BDL
147
4,748
0
04 Jan 2016
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with
  Theoretical Guarantees
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
François‐Xavier Briol
Chris J. Oates
Mark Girolami
Michael A. Osborne
48
89
0
08 Jun 2015
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based
  Statistical Models
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
Michael U. Gutmann
J. Corander
78
285
0
14 Jan 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
421
149,474
0
22 Dec 2014
Sampling for Inference in Probabilistic Models with Fast Bayesian
  Quadrature
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
Tom Gunter
Michael A. Osborne
Roman Garnett
Philipp Hennig
Stephen J. Roberts
TPM
29
104
0
03 Nov 2014
Auto-Encoding Variational Bayes
Auto-Encoding Variational Bayes
Diederik P. Kingma
Max Welling
BDL
315
16,972
0
20 Dec 2013
Practical Bayesian Optimization of Machine Learning Algorithms
Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek
Hugo Larochelle
Ryan P. Adams
240
7,883
0
13 Jun 2012
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with
  Application to Active User Modeling and Hierarchical Reinforcement Learning
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
E. Brochu
Vlad M. Cora
Nando de Freitas
GP
91
2,437
0
12 Dec 2010
Cases for the nugget in modeling computer experiments
Cases for the nugget in modeling computer experiments
R. Gramacy
Herbert K. H. Lee
98
279
0
26 Jul 2010
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