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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
Interpreting diffusion score matching using normalizing flow
Interpreting diffusion score matching using normalizing flow
Wenbo Gong
Yingzhen Li
DiffM
27
13
0
21 Jul 2021
Stein Variational Gradient Descent with Multiple Kernel
Stein Variational Gradient Descent with Multiple Kernel
Qingzhong Ai
Shiyu Liu
Lirong He
Zenglin Xu
22
4
0
20 Jul 2021
The Causal-Neural Connection: Expressiveness, Learnability, and
  Inference
The Causal-Neural Connection: Expressiveness, Learnability, and Inference
K. Xia
Kai-Zhan Lee
Yoshua Bengio
Elias Bareinboim
CML
20
104
0
02 Jul 2021
Three rates of convergence or separation via U-statistics in a dependent
  framework
Three rates of convergence or separation via U-statistics in a dependent framework
Quentin Duchemin
Yohann De Castro
C. Lacour
8
0
0
24 Jun 2021
Sampling with Mirrored Stein Operators
Sampling with Mirrored Stein Operators
Jiaxin Shi
Chang-rui Liu
Lester W. Mackey
45
19
0
23 Jun 2021
Standardisation-function Kernel Stein Discrepancy: A Unifying View on
  Kernel Stein Discrepancy Tests for Goodness-of-fit
Standardisation-function Kernel Stein Discrepancy: A Unifying View on Kernel Stein Discrepancy Tests for Goodness-of-fit
Wenkai Xu
32
4
0
23 Jun 2021
Repulsive Deep Ensembles are Bayesian
Repulsive Deep Ensembles are Bayesian
Francesco DÁngelo
Vincent Fortuin
UQCV
BDL
51
93
0
22 Jun 2021
Non Gaussian Denoising Diffusion Models
Non Gaussian Denoising Diffusion Models
Eliya Nachmani
Robin San Roman
Lior Wolf
VLM
DiffM
32
48
0
14 Jun 2021
Separation Results between Fixed-Kernel and Feature-Learning Probability
  Metrics
Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics
Carles Domingo-Enrich
Youssef Mroueh
21
1
0
10 Jun 2021
Detecting Anomalous Event Sequences with Temporal Point Processes
Detecting Anomalous Event Sequences with Temporal Point Processes
Oleksandr Shchur
Ali Caner Turkmen
Tim Januschowski
Jan Gasthaus
Stephan Günnemann
AI4TS
20
12
0
08 Jun 2021
Instrument Space Selection for Kernel Maximum Moment Restriction
Instrument Space Selection for Kernel Maximum Moment Restriction
Rui Zhang
Krikamol Muandet
Bernhard Schölkopf
Masaaki Imaizumi
14
3
0
07 Jun 2021
Stein ICP for Uncertainty Estimation in Point Cloud Matching
Stein ICP for Uncertainty Estimation in Point Cloud Matching
F. A. Maken
Fabio Ramos
Lionel Ott
3DV
3DPC
26
25
0
07 Jun 2021
A Convergence Theory for SVGD in the Population Limit under Talagrand's
  Inequality T1
A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1
Adil Salim
Lukang Sun
Peter Richtárik
23
20
0
06 Jun 2021
Efficient and Generalizable Tuning Strategies for Stochastic Gradient
  MCMC
Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC
Jeremie Coullon
Leah F. South
Christopher Nemeth
11
12
0
27 May 2021
Statistical Depth Meets Machine Learning: Kernel Mean Embeddings and
  Depth in Functional Data Analysis
Statistical Depth Meets Machine Learning: Kernel Mean Embeddings and Depth in Functional Data Analysis
George Wynne
Stanislav Nagy
11
5
0
26 May 2021
Kernel Stein Discrepancy Descent
Kernel Stein Discrepancy Descent
Anna Korba
Pierre-Cyril Aubin-Frankowski
Szymon Majewski
Pierre Ablin
16
50
0
20 May 2021
Stein's Method Meets Computational Statistics: A Review of Some Recent
  Developments
Stein's Method Meets Computational Statistics: A Review of Some Recent Developments
Andreas Anastasiou
Alessandro Barp
F. Briol
B. Ebner
Robert E. Gaunt
...
Qiang Liu
Lester W. Mackey
Chris J. Oates
Gesine Reinert
Yvik Swan
22
35
0
07 May 2021
A Unifying and Canonical Description of Measure-Preserving Diffusions
A Unifying and Canonical Description of Measure-Preserving Diffusions
Alessandro Barp
So Takao
M. Betancourt
Alexis Arnaudon
Mark Girolami
22
17
0
06 May 2021
On Energy-Based Models with Overparametrized Shallow Neural Networks
On Energy-Based Models with Overparametrized Shallow Neural Networks
Carles Domingo-Enrich
A. Bietti
Eric Vanden-Eijnden
Joan Bruna
BDL
19
9
0
15 Apr 2021
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
24
74
0
15 Apr 2021
Noise Estimation for Generative Diffusion Models
Noise Estimation for Generative Diffusion Models
Robin San-Roman
Eliya Nachmani
Lior Wolf
DiffM
28
105
0
06 Apr 2021
Post-Processing of MCMC
Post-Processing of MCMC
Leah F. South
M. Riabiz
Onur Teymur
Chris J. Oates
19
17
0
30 Mar 2021
Posterior Meta-Replay for Continual Learning
Posterior Meta-Replay for Continual Learning
Christian Henning
Maria R. Cervera
Francesco DÁngelo
J. Oswald
Regina Traber
Benjamin Ehret
Seijin Kobayashi
Benjamin Grewe
João Sacramento
CLL
BDL
51
54
0
01 Mar 2021
A Stein Goodness of fit Test for Exponential Random Graph Models
A Stein Goodness of fit Test for Exponential Random Graph Models
Wenkai Xu
Gesine Reinert
21
5
0
28 Feb 2021
Stein Variational Gradient Descent: many-particle and long-time
  asymptotics
Stein Variational Gradient Descent: many-particle and long-time asymptotics
Nikolas Nusken
D. M. Renger
27
22
0
25 Feb 2021
Product-form estimators: exploiting independence to scale up Monte Carlo
Product-form estimators: exploiting independence to scale up Monte Carlo
Juan Kuntz
F. R. Crucinio
A. M. Johansen
28
10
0
23 Feb 2021
Tractable Computation of Expected Kernels
Tractable Computation of Expected Kernels
Wenzhe Li
Zhe Zeng
Antonio Vergari
Guy Van den Broeck
TPM
18
1
0
21 Feb 2021
Active Slices for Sliced Stein Discrepancy
Active Slices for Sliced Stein Discrepancy
Wenbo Gong
Kaibo Zhang
Yingzhen Li
José Miguel Hernández-Lobato
22
8
0
05 Feb 2021
Benchmarking Simulation-Based Inference
Benchmarking Simulation-Based Inference
Jan-Matthis Lueckmann
Jan Boelts
David S. Greenberg
P. J. Gonçalves
Jakob H. Macke
104
185
0
12 Jan 2021
How to Train Your Energy-Based Models
How to Train Your Energy-Based Models
Yang Song
Diederik P. Kingma
DiffM
24
241
0
09 Jan 2021
Integrable Nonparametric Flows
Integrable Nonparametric Flows
David Pfau
Danilo Jimenez Rezende
6
5
0
03 Dec 2020
Diffusion models for Handwriting Generation
Diffusion models for Handwriting Generation
Troy Luhman
Eric Luhman
DiffM
19
25
0
13 Nov 2020
Dimension-agnostic inference using cross U-statistics
Dimension-agnostic inference using cross U-statistics
Ilmun Kim
Aaditya Ramdas
19
16
0
10 Nov 2020
Characterizations of non-normalized discrete probability distributions
  and their application in statistics
Characterizations of non-normalized discrete probability distributions and their application in statistics
Steffen Betsch
B. Ebner
F. Nestmann
11
13
0
09 Nov 2020
Measure Transport with Kernel Stein Discrepancy
Measure Transport with Kernel Stein Discrepancy
Matthew A. Fisher
T. Nolan
Matthew M. Graham
D. Prangle
Chris J. Oates
OT
31
15
0
22 Oct 2020
Variational (Gradient) Estimate of the Score Function in Energy-based
  Latent Variable Models
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
Fan Bao
Kun Xu
Chongxuan Li
Lanqing Hong
Jun Zhu
Bo Zhang
DiffM
22
8
0
16 Oct 2020
Auxiliary Task Reweighting for Minimum-data Learning
Auxiliary Task Reweighting for Minimum-data Learning
Baifeng Shi
Judy Hoffman
Kate Saenko
Trevor Darrell
Huijuan Xu
MoMe
27
32
0
16 Oct 2020
Testing for Normality with Neural Networks
Testing for Normality with Neural Networks
M. Simic
19
6
0
29 Sep 2020
Stein Variational Gaussian Processes
Stein Variational Gaussian Processes
Thomas Pinder
Christopher Nemeth
David Leslie
BDL
6
7
0
25 Sep 2020
Federated Generalized Bayesian Learning via Distributed Stein
  Variational Gradient Descent
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent
Rahif Kassab
Osvaldo Simeone
FedML
23
45
0
11 Sep 2020
Blindness of score-based methods to isolated components and mixing
  proportions
Blindness of score-based methods to isolated components and mixing proportions
Wenliang K. Li
Heishiro Kanagawa
17
34
0
23 Aug 2020
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event
  Data
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data
T. Fernandez
Nicolás Rivera
Wenkai Xu
A. Gretton
6
15
0
19 Aug 2020
Learning Gradient Fields for Shape Generation
Learning Gradient Fields for Shape Generation
Ruojin Cai
Guandao Yang
Hadar Averbuch-Elor
Jinwei Gu
Serge J. Belongie
Noah Snavely
B. Hariharan
3DPC
19
280
0
14 Aug 2020
Fisher Auto-Encoders
Fisher Auto-Encoders
Khalil Elkhalil
Ali Hasan
Jie Ding
Sina Farsiu
Vahid Tarokh
9
9
0
12 Jul 2020
Efficient Learning of Generative Models via Finite-Difference Score
  Matching
Efficient Learning of Generative Models via Finite-Difference Score Matching
Tianyu Pang
Kun Xu
Chongxuan Li
Yang Song
Stefano Ermon
Jun Zhu
DiffM
31
53
0
07 Jul 2020
Kernel Stein Generative Modeling
Kernel Stein Generative Modeling
Wei-Cheng Chang
Chun-Liang Li
Youssef Mroueh
Yiming Yang
DiffM
BDL
33
5
0
06 Jul 2020
Stochastic Stein Discrepancies
Stochastic Stein Discrepancies
Jackson Gorham
Anant Raj
Lester W. Mackey
19
37
0
06 Jul 2020
Sliced Kernelized Stein Discrepancy
Sliced Kernelized Stein Discrepancy
Wenbo Gong
Yingzhen Li
José Miguel Hernández-Lobato
20
37
0
30 Jun 2020
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
A Non-Asymptotic Analysis for Stein Variational Gradient Descent
Anna Korba
Adil Salim
Michael Arbel
Giulia Luise
A. Gretton
13
76
0
17 Jun 2020
Scalable Control Variates for Monte Carlo Methods via Stochastic
  Optimization
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization
Shijing Si
Chris J. Oates
Andrew B. Duncan
Lawrence Carin
F. Briol
BDL
15
21
0
12 Jun 2020
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