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Optimally-Weighted Estimators of the Maximum Mean Discrepancy for
  Likelihood-Free Inference
v1v2v3v4 (latest)

Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference

27 January 2023
Ayush Bharti
Masha Naslidnyk
Oscar Key
Samuel Kaski
F. Briol
ArXiv (abs)PDFHTML

Papers citing "Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference"

47 / 47 papers shown
Title
Kernel Quantile Embeddings and Associated Probability Metrics
Kernel Quantile Embeddings and Associated Probability Metrics
Masha Naslidnyk
Siu Lun Chau
F. Briol
Krikamol Muandet
70
0
0
26 May 2025
Robust Bayesian Inference for Simulator-based Models via the MMD
  Posterior Bootstrap
Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap
Charita Dellaporta
Jeremias Knoblauch
Theodoros Damoulas
F. Briol
74
45
0
09 Feb 2022
Approximate Bayesian Computation with Domain Expert in the Loop
Approximate Bayesian Computation with Domain Expert in the Loop
Ayush Bharti
Louis Filstroff
Samuel Kaski
TPM
110
9
0
28 Jan 2022
ProbNum: Probabilistic Numerics in Python
ProbNum: Probabilistic Numerics in Python
Jonathan Wenger
Nicholas Kramer
Marvin Pfortner
Jonathan Schmidt
Nathanael Bosch
...
A. Gessner
Toni Karvonen
F. Briol
Maren Mahsereci
Philipp Hennig
89
17
0
03 Dec 2021
Composite Goodness-of-fit Tests with Kernels
Composite Goodness-of-fit Tests with Kernels
Oscar Key
Arthur Gretton
F. Briol
T. Fernandez
86
16
0
19 Nov 2021
Discrepancy-based Inference for Intractable Generative Models using
  Quasi-Monte Carlo
Discrepancy-based Inference for Intractable Generative Models using Quasi-Monte Carlo
Ziang Niu
J. Meier
F. Briol
68
13
0
22 Jun 2021
Kernel Thinning
Kernel Thinning
Raaz Dwivedi
Lester W. Mackey
168
39
0
12 May 2021
Sequential Neural Posterior and Likelihood Approximation
Sequential Neural Posterior and Likelihood Approximation
Samuel Wiqvist
J. Frellsen
Umberto Picchini
BDL
415
33
0
12 Feb 2021
Benchmarking Simulation-Based Inference
Benchmarking Simulation-Based Inference
Jan-Matthis Lueckmann
Jan Boelts
David S. Greenberg
P. J. Gonçalves
Jakob H. Macke
259
198
0
12 Jan 2021
Universal Robust Regression via Maximum Mean Discrepancy
Universal Robust Regression via Maximum Mean Discrepancy
Pierre Alquier
Mathieu Gerber
76
16
0
01 Jun 2020
Optimal Thinning of MCMC Output
Optimal Thinning of MCMC Output
M. Riabiz
W. Chen
Jon Cockayne
P. Swietach
Steven Niederer
Lester W. Mackey
Chris J. Oates
62
47
0
08 May 2020
Convergence Guarantees for Gaussian Process Means With Misspecified
  Likelihoods and Smoothness
Convergence Guarantees for Gaussian Process Means With Misspecified Likelihoods and Smoothness
George Wynne
F. Briol
Mark Girolami
65
56
0
29 Jan 2020
The frontier of simulation-based inference
The frontier of simulation-based inference
Kyle Cranmer
Johann Brehmer
Gilles Louppe
AI4CE
190
854
0
04 Nov 2019
MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy
MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy
Badr-Eddine Chérief-Abdellatif
Pierre Alquier
161
75
0
29 Sep 2019
Convergence of Gaussian Process Regression with Estimated
  Hyper-parameters and Applications in Bayesian Inverse Problems
Convergence of Gaussian Process Regression with Estimated Hyper-parameters and Applications in Bayesian Inverse Problems
A. Teckentrup
42
67
0
31 Aug 2019
Kernel quadrature with DPPs
Kernel quadrature with DPPs
Ayoub Belhadji
Rémi Bardenet
P. Chainais
65
41
0
18 Jun 2019
Statistical Inference for Generative Models with Maximum Mean
  Discrepancy
Statistical Inference for Generative Models with Maximum Mean Discrepancy
François‐Xavier Briol
Alessandro Barp
Andrew B. Duncan
Mark Girolami
53
72
0
13 Jun 2019
Automatic Posterior Transformation for Likelihood-Free Inference
Automatic Posterior Transformation for Likelihood-Free Inference
David S. Greenberg
M. Nonnenmacher
Jakob H. Macke
387
331
0
17 May 2019
Approximate Bayesian computation via the energy statistic
Approximate Bayesian computation via the energy statistic
Hien Nguyen
Julyan Arbel
Hongliang Lü
F. Forbes
87
29
0
14 May 2019
Stein Point Markov Chain Monte Carlo
Stein Point Markov Chain Monte Carlo
W. Chen
Alessandro Barp
François‐Xavier Briol
Jackson Gorham
Mark Girolami
Lester W. Mackey
Chris J. Oates
81
57
0
09 May 2019
Sample Complexity of Sinkhorn divergences
Sample Complexity of Sinkhorn divergences
Aude Genevay
Lénaïc Chizat
Francis R. Bach
Marco Cuturi
Gabriel Peyré
OT
77
289
0
05 Oct 2018
Symmetry Exploits for Bayesian Cubature Methods
Symmetry Exploits for Bayesian Cubature Methods
Toni Karvonen
Simo Särkkä
Chris J. Oates
38
15
0
26 Sep 2018
Fast Automatic Bayesian Cubature Using Lattice Sampling
Fast Automatic Bayesian Cubature Using Lattice Sampling
R. Jagadeeswaran
F. J. Hickernell
30
36
0
26 Sep 2018
Stein Points
Stein Points
W. Chen
Lester W. Mackey
Jackson Gorham
François‐Xavier Briol
Chris J. Oates
71
102
0
27 Mar 2018
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
T. Kajihara
Motonobu Kanagawa
Keisuke Yamazaki
Kenji Fukumizu
70
13
0
23 Feb 2018
Demystifying MMD GANs
Demystifying MMD GANs
Mikolaj Binkowski
Danica J. Sutherland
Michael Arbel
Arthur Gretton
EGVM
174
1,500
0
04 Jan 2018
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in
  Misspecified Settings
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings
Motonobu Kanagawa
Bharath K. Sriperumbudur
Kenji Fukumizu
83
45
0
01 Sep 2017
Learning Generative Models with Sinkhorn Divergences
Learning Generative Models with Sinkhorn Divergences
Aude Genevay
Gabriel Peyré
Marco Cuturi
OT
190
631
0
01 Jun 2017
MMD GAN: Towards Deeper Understanding of Moment Matching Network
MMD GAN: Towards Deeper Understanding of Moment Matching Network
Chun-Liang Li
Wei-Cheng Chang
Yu Cheng
Yiming Yang
Barnabás Póczós
GAN
66
724
0
24 May 2017
Support points
Support points
Simon Mak
V. R. Joseph
51
141
0
07 Sep 2016
Uncertain programming model for multi-item solid transportation problem
Uncertain programming model for multi-item solid transportation problem
Hasan Dalman
120
744
0
31 May 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
28
33
0
15 Feb 2016
Probabilistic Integration: A Role in Statistical Computation?
Probabilistic Integration: A Role in Statistical Computation?
François‐Xavier Briol
Chris J. Oates
Mark Girolami
Michael A. Osborne
Dino Sejdinovic
62
53
0
03 Dec 2015
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
91
90
0
08 Jun 2015
Training generative neural networks via Maximum Mean Discrepancy
  optimization
Training generative neural networks via Maximum Mean Discrepancy optimization
Gintare Karolina Dziugaite
Daniel M. Roy
Zoubin Ghahramani
GAN
84
530
0
14 May 2015
Approximate Bayesian Computation for Forward Modeling in Cosmology
Approximate Bayesian Computation for Forward Modeling in Cosmology
Joel Akeret
Alexandre Réfrégier
A. Amara
Sebastian Seehars
C. Hasner
47
90
0
27 Apr 2015
Extending approximate Bayesian computation methods to high dimensions
  via a Gaussian copula model
Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model
Jingjing Li
David J. Nott
Yanan Fan
Scott A. Sisson
174
42
0
16 Apr 2015
Generative Moment Matching Networks
Generative Moment Matching Networks
Yujia Li
Kevin Swersky
R. Zemel
OODGAN
116
847
0
10 Feb 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
169
288
0
14 Jan 2015
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
64
104
0
03 Nov 2014
Likelihood-free inference via classification
Likelihood-free inference via classification
Michael U. Gutmann
Ritabrata Dutta
Samuel Kaski
J. Corander
186
63
0
18 Jul 2014
On the Equivalence between Herding and Conditional Gradient Algorithms
On the Equivalence between Herding and Conditional Gradient Algorithms
Francis R. Bach
Simon Lacoste-Julien
G. Obozinski
182
169
0
20 Mar 2012
Super-Samples from Kernel Herding
Super-Samples from Kernel Herding
Yutian Chen
Max Welling
Alex Smola
167
342
0
15 Mar 2012
Approximate Bayesian Computational methods
Approximate Bayesian Computational methods
Jean-Michel Marin
Pierre Pudlo
Christian P. Robert
Robin J. Ryder
234
866
0
05 Jan 2011
Hilbert space embeddings and metrics on probability measures
Hilbert space embeddings and metrics on probability measures
Bharath K. Sriperumbudur
Arthur Gretton
Kenji Fukumizu
Bernhard Schölkopf
Gert R. G. Lanckriet
217
747
0
30 Jul 2009
A Kernel Method for the Two-Sample Problem
A Kernel Method for the Two-Sample Problem
Arthur Gretton
Karsten Borgwardt
Malte J. Rasch
Bernhard Schölkopf
Alex Smola
233
2,365
0
15 May 2008
Adaptive approximate Bayesian computation
Adaptive approximate Bayesian computation
Mark Beaumont
J. Cornuet
Jean-Michel Marin
Christian P. Robert
203
646
0
15 May 2008
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