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A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model
  Evaluation

A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation

10 February 2016
Qiang Liu
J. Lee
Michael I. Jordan
ArXivPDFHTML

Papers citing "A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation"

50 / 296 papers shown
Title
Constrained Stein Variational Trajectory Optimization
Constrained Stein Variational Trajectory Optimization
Thomas Power
Dmitry Berenson
33
12
0
23 Aug 2023
Semi-Implicit Variational Inference via Score Matching
Semi-Implicit Variational Inference via Score Matching
Longlin Yu
C. Zhang
19
10
0
19 Aug 2023
Spectral Regularized Kernel Goodness-of-Fit Tests
Spectral Regularized Kernel Goodness-of-Fit Tests
Omar Hagrass
Bharath K. Sriperumbudur
Bing Li
29
3
0
08 Aug 2023
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
Tobias Schröder
Zijing Ou
Jen Ning Lim
Yingzhen Li
Sebastian J. Vollmer
Andrew B. Duncan
30
4
0
12 Jul 2023
A prior regularized full waveform inversion using generative diffusion
  models
A prior regularized full waveform inversion using generative diffusion models
Fu Wang
Xinquan Huang
T. Alkhalifah
DiffM
40
25
0
22 Jun 2023
MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without
  Data Splitting
MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting
Felix Biggs
Antonin Schrab
A. Gretton
17
19
0
14 Jun 2023
Convergence of mean-field Langevin dynamics: Time and space
  discretization, stochastic gradient, and variance reduction
Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction
Taiji Suzuki
Denny Wu
Atsushi Nitanda
29
16
0
12 Jun 2023
Entropy-based Training Methods for Scalable Neural Implicit Sampler
Entropy-based Training Methods for Scalable Neural Implicit Sampler
Weijian Luo
Boya Zhang
Zhihua Zhang
31
10
0
08 Jun 2023
GANs Settle Scores!
GANs Settle Scores!
Siddarth Asokan
Nishanth Shetty
Aadithya Srikanth
C. Seelamantula
42
0
0
02 Jun 2023
Approximate Stein Classes for Truncated Density Estimation
Approximate Stein Classes for Truncated Density Estimation
Daniel J. Williams
Song Liu
13
0
0
01 Jun 2023
Provably Fast Finite Particle Variants of SVGD via Virtual Particle
  Stochastic Approximation
Provably Fast Finite Particle Variants of SVGD via Virtual Particle Stochastic Approximation
Aniket Das
Dheeraj M. Nagaraj
35
7
0
27 May 2023
Non-adversarial training of Neural SDEs with signature kernel scores
Non-adversarial training of Neural SDEs with signature kernel scores
Zacharia Issa
Blanka Horvath
M. Lemercier
C. Salvi
AI4TS
37
24
0
25 May 2023
Learning Rate Free Sampling in Constrained Domains
Learning Rate Free Sampling in Constrained Domains
Louis Sharrock
Lester W. Mackey
Christopher Nemeth
38
2
0
24 May 2023
Towards Understanding the Dynamics of Gaussian-Stein Variational
  Gradient Descent
Towards Understanding the Dynamics of Gaussian-Stein Variational Gradient Descent
Tianle Liu
Promit Ghosal
Krishnakumar Balasubramanian
Natesh S. Pillai
27
9
0
23 May 2023
Kernel Stein Discrepancy on Lie Groups: Theory and Applications
Kernel Stein Discrepancy on Lie Groups: Theory and Applications
Xiaoda Qu
Xiran Fan
B. Vemuri
32
0
0
21 May 2023
Stein $Π$-Importance Sampling
Stein ΠΠΠ-Importance Sampling
Congye Wang
Ye Chen
Heishiro Kanagawa
Chris J. Oates
37
2
0
17 May 2023
Differentiable Neural Networks with RePU Activation: with Applications
  to Score Estimation and Isotonic Regression
Differentiable Neural Networks with RePU Activation: with Applications to Score Estimation and Isotonic Regression
Guohao Shen
Yuling Jiao
Yuanyuan Lin
Jian Huang
50
3
0
01 May 2023
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized
  Stein Discrepancy
Using Perturbation to Improve Goodness-of-Fit Tests based on Kernelized Stein Discrepancy
Xingtu Liu
Andrew B. Duncan
Axel Gandy
35
7
0
28 Apr 2023
The Score-Difference Flow for Implicit Generative Modeling
The Score-Difference Flow for Implicit Generative Modeling
Romann M. Weber
DiffM
29
2
0
25 Apr 2023
Causal Discovery with Score Matching on Additive Models with Arbitrary
  Noise
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise
Francesco Montagna
Nicoletta Noceti
Lorenzo Rosasco
Anton van den Hengel
Francesco Locatello
CML
13
27
0
06 Apr 2023
A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy
  Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial
  Networks
A Semi-Bayesian Nonparametric Estimator of the Maximum Mean Discrepancy Measure: Applications in Goodness-of-Fit Testing and Generative Adversarial Networks
Forough Fazeli Asl
M. Zhang
Lizhen Lin
24
1
0
05 Mar 2023
How to Trust Your Diffusion Model: A Convex Optimization Approach to
  Conformal Risk Control
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
Jacopo Teneggi
Matthew Tivnan
J. W. Stayman
Jeremias Sulam
DiffM
35
28
0
07 Feb 2023
Kernel Stein Discrepancy thinning: a theoretical perspective of
  pathologies and a practical fix with regularization
Kernel Stein Discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization
Clément Bénard
B. Staber
Sébastien Da Veiga
38
4
0
31 Jan 2023
STEERING: Stein Information Directed Exploration for Model-Based
  Reinforcement Learning
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning
Souradip Chakraborty
Amrit Singh Bedi
Alec Koppel
Mengdi Wang
Furong Huang
Dinesh Manocha
24
7
0
28 Jan 2023
Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated
  Models
Minimax Optimal Rate for Parameter Estimation in Multivariate Deviated Models
Dat Do
Huy Nguyen
Khai Nguyen
Nhat Ho
23
4
0
27 Jan 2023
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
Louis Sharrock
Christopher Nemeth
BDL
28
8
0
26 Jan 2023
Separate And Diffuse: Using a Pretrained Diffusion Model for Improving
  Source Separation
Separate And Diffuse: Using a Pretrained Diffusion Model for Improving Source Separation
Shahar Lutati
Eliya Nachmani
Lior Wolf
DiffM
36
14
0
25 Jan 2023
Client Selection for Federated Bayesian Learning
Client Selection for Federated Bayesian Learning
Jiarong Yang
Yuan Liu
Rahif Kassab
FedML
38
11
0
11 Dec 2022
Efficient Stein Variational Inference for Reliable Distribution-lossless
  Network Pruning
Efficient Stein Variational Inference for Reliable Distribution-lossless Network Pruning
Yingchun Wang
Song Guo
Jingcai Guo
Weizhan Zhang
Yi Tian Xu
Jiewei Zhang
Yi Liu
21
17
0
07 Dec 2022
Are you using test log-likelihood correctly?
Are you using test log-likelihood correctly?
Sameer K. Deshpande
Soumya K. Ghosh
Tin D. Nguyen
Tamara Broderick
32
7
0
01 Dec 2022
Particle-based Variational Inference with Preconditioned Functional
  Gradient Flow
Particle-based Variational Inference with Preconditioned Functional Gradient Flow
Hanze Dong
Xi Wang
Yong Lin
Tong Zhang
27
19
0
25 Nov 2022
A Finite-Particle Convergence Rate for Stein Variational Gradient
  Descent
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent
Jiaxin Shi
Lester W. Mackey
28
18
0
17 Nov 2022
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres
S. Hubbert
Emilio Porcu
Chris J. Oates
Mark Girolami
11
4
0
16 Nov 2022
Regularized Stein Variational Gradient Flow
Regularized Stein Variational Gradient Flow
Ye He
Krishnakumar Balasubramanian
Bharath K. Sriperumbudur
Jianfeng Lu
OT
34
11
0
15 Nov 2022
Aspects of scaling and scalability for flow-based sampling of lattice
  QCD
Aspects of scaling and scalability for flow-based sampling of lattice QCD
Ryan Abbott
M. S. Albergo
Aleksandar Botev
D. Boyda
Kyle Cranmer
...
Ali Razavi
Danilo Jimenez Rezende
F. Romero-López
P. Shanahan
Julian M. Urban
32
33
0
14 Nov 2022
Controlling Moments with Kernel Stein Discrepancies
Controlling Moments with Kernel Stein Discrepancies
Heishiro Kanagawa
Alessandro Barp
A. Gretton
Lester W. Mackey
24
8
0
10 Nov 2022
Ensemble transport smoothing. Part II: Nonlinear updates
Ensemble transport smoothing. Part II: Nonlinear updates
M. Ramgraber
Ricardo Baptista
D. McLaughlin
Youssef Marzouk
23
6
0
31 Oct 2022
Minimum Kernel Discrepancy Estimators
Minimum Kernel Discrepancy Estimators
Chris J. Oates
27
10
0
28 Oct 2022
Preferential Subsampling for Stochastic Gradient Langevin Dynamics
Preferential Subsampling for Stochastic Gradient Langevin Dynamics
Srshti Putcha
Christopher Nemeth
Paul Fearnhead
22
0
0
28 Oct 2022
MARS: Meta-Learning as Score Matching in the Function Space
MARS: Meta-Learning as Score Matching in the Function Space
Krunoslav Lehman Pavasovic
Jonas Rothfuss
Andreas Krause
BDL
30
4
0
24 Oct 2022
A kernel Stein test of goodness of fit for sequential models
A kernel Stein test of goodness of fit for sequential models
Jerome Baum
Heishiro Kanagawa
A. Gretton
24
9
0
19 Oct 2022
Transport Elliptical Slice Sampling
Transport Elliptical Slice Sampling
A. Cabezas
Christopher Nemeth
16
8
0
19 Oct 2022
Auto-Encoding Goodness of Fit
Auto-Encoding Goodness of Fit
A. Palmer
Zhiyi Chi
Derek Aguiar
J. Bi
41
1
0
12 Oct 2022
On RKHS Choices for Assessing Graph Generators via Kernel Stein
  Statistics
On RKHS Choices for Assessing Graph Generators via Kernel Stein Statistics
Moritz Weckbecker
Wenkai Xu
Gesine Reinert
52
3
0
11 Oct 2022
Sequential Neural Score Estimation: Likelihood-Free Inference with
  Conditional Score Based Diffusion Models
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
Louis Sharrock
J. Simons
Song Liu
Mark Beaumont
DiffM
61
33
0
10 Oct 2022
Hiding Images in Deep Probabilistic Models
Hiding Images in Deep Probabilistic Models
Haoyu Chen
Linqi Song
Zhenxing Qian
Xinpeng Zhang
Kede Ma
AAML
18
10
0
05 Oct 2022
How good is your Laplace approximation of the Bayesian posterior?
  Finite-sample computable error bounds for a variety of useful divergences
How good is your Laplace approximation of the Bayesian posterior? Finite-sample computable error bounds for a variety of useful divergences
Mikolaj Kasprzak
Ryan Giordano
Tamara Broderick
33
4
0
29 Sep 2022
Targeted Separation and Convergence with Kernel Discrepancies
Targeted Separation and Convergence with Kernel Discrepancies
Alessandro Barp
Carl-Johann Simon-Gabriel
Mark Girolami
Lester W. Mackey
48
14
0
26 Sep 2022
Amortized Variational Inference: A Systematic Review
Amortized Variational Inference: A Systematic Review
Ankush Ganguly
Sanjana Jain
Ukrit Watchareeruetai
25
14
0
22 Sep 2022
Towards Healing the Blindness of Score Matching
Towards Healing the Blindness of Score Matching
Mingtian Zhang
Oscar Key
Peter Hayes
David Barber
Brooks Paige
F. Briol
MedIm
55
14
0
15 Sep 2022
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