ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1602.03253
  4. Cited By
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
MDM: Molecular Diffusion Model for 3D Molecule Generation
MDM: Molecular Diffusion Model for 3D Molecule Generation
Lei Huang
Hengtong Zhang
Tingyang Xu
Ka-Chun Wong
DiffM
18
81
0
13 Sep 2022
A general framework for the analysis of kernel-based tests
A general framework for the analysis of kernel-based tests
Tamara Fernández
Nicolás Rivera
11
4
0
31 Aug 2022
A deep learning framework for geodesics under spherical
  Wasserstein-Fisher-Rao metric and its application for weighted sample
  generation
A deep learning framework for geodesics under spherical Wasserstein-Fisher-Rao metric and its application for weighted sample generation
Yang Jing
Jia-hua Chen
Lei Li
Jianfeng Lu
17
1
0
25 Aug 2022
A Survey of Learning on Small Data: Generalization, Optimization, and
  Challenge
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge
Xiaofeng Cao
Weixin Bu
Sheng-Jun Huang
Minling Zhang
Ivor W. Tsang
Yew-Soon Ong
James T. Kwok
35
1
0
29 Jul 2022
Neural Stein critics with staged $L^2$-regularization
Neural Stein critics with staged L2L^2L2-regularization
Matthew Repasky
Xiuyuan Cheng
Yao Xie
19
3
0
07 Jul 2022
Gradient-Free Kernel Stein Discrepancy
Gradient-Free Kernel Stein Discrepancy
Matthew A. Fisher
Chris J. Oates
20
5
0
06 Jul 2022
Learning to Increase the Power of Conditional Randomization Tests
Learning to Increase the Power of Conditional Randomization Tests
Shalev Shaer
Yaniv Romano
CML
26
2
0
03 Jul 2022
Score Matching for Truncated Density Estimation on a Manifold
Score Matching for Truncated Density Estimation on a Manifold
Daniel J. Williams
Song Liu
12
3
0
29 Jun 2022
Efficient Aggregated Kernel Tests using Incomplete $U$-statistics
Efficient Aggregated Kernel Tests using Incomplete UUU-statistics
Antonin Schrab
Ilmun Kim
Benjamin Guedj
A. Gretton
32
29
0
18 Jun 2022
Stein Variational Goal Generation for adaptive Exploration in Multi-Goal
  Reinforcement Learning
Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning
Nicolas Castanet
Sylvain Lamprier
Olivier Sigaud
17
2
0
14 Jun 2022
A Fourier representation of kernel Stein discrepancy with application to
  Goodness-of-Fit tests for measures on infinite dimensional Hilbert spaces
A Fourier representation of kernel Stein discrepancy with application to Goodness-of-Fit tests for measures on infinite dimensional Hilbert spaces
George Wynne
Mikolaj Kasprzak
Andrew B. Duncan
25
4
0
09 Jun 2022
Benchmarking Bayesian neural networks and evaluation metrics for
  regression tasks
Benchmarking Bayesian neural networks and evaluation metrics for regression tasks
B. Staber
Sébastien Da Veiga
UQCV
BDL
42
3
0
08 Jun 2022
Posterior Coreset Construction with Kernelized Stein Discrepancy for
  Model-Based Reinforcement Learning
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning
Souradip Chakraborty
Amrit Singh Bedi
Alec Koppel
Brian M. Sadler
Furong Huang
Pratap Tokekar
Tianyi Zhou
25
9
0
02 Jun 2022
Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in
  Offline RL
Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL
Wonjoon Goo
S. Niekum
OffRL
21
20
0
01 Jun 2022
Convergence of Stein Variational Gradient Descent under a Weaker
  Smoothness Condition
Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition
Lukang Sun
Avetik G. Karagulyan
Peter Richtárik
26
19
0
01 Jun 2022
A Kernelised Stein Statistic for Assessing Implicit Generative Models
A Kernelised Stein Statistic for Assessing Implicit Generative Models
Wenkai Xu
Gesine Reinert
SyDa
18
3
0
31 May 2022
MixFlows: principled variational inference via mixed flows
MixFlows: principled variational inference via mixed flows
Zuheng Xu
Na Chen
Trevor Campbell
55
8
0
16 May 2022
Geometric Methods for Sampling, Optimisation, Inference and Adaptive
  Agents
Geometric Methods for Sampling, Optimisation, Inference and Adaptive Agents
Alessandro Barp
Lancelot Da Costa
G. Francca
Karl J. Friston
Mark Girolami
Michael I. Jordan
G. Pavliotis
31
25
0
20 Mar 2022
Score matching enables causal discovery of nonlinear additive noise
  models
Score matching enables causal discovery of nonlinear additive noise models
Paul Rolland
V. Cevher
Matthäus Kleindessner
Chris Russel
Bernhard Schölkopf
Dominik Janzing
Francesco Locatello
CML
54
84
0
08 Mar 2022
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment
  of Implicit Graph Generators
AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators
Wenkai Xu
Gesine Reinert
27
4
0
07 Mar 2022
Score-Based Generative Models for Molecule Generation
Score-Based Generative Models for Molecule Generation
Dwaraknath Gnaneshwar
Bharath Ramsundar
Dhairya Gandhi
Rachel C. Kurchin
V. Viswanathan
DiffM
22
11
0
07 Mar 2022
Stochastic Modeling of Inhomogeneities in the Aortic Wall and
  Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate
Stochastic Modeling of Inhomogeneities in the Aortic Wall and Uncertainty Quantification using a Bayesian Encoder-Decoder Surrogate
Sascha Ranftl
Malte Rolf-Pissarczyk
G. Wolkerstorfer
Antonio Pepe
Jan Egger
W. Linden
G. Holzapfel
31
9
0
21 Feb 2022
Understanding DDPM Latent Codes Through Optimal Transport
Understanding DDPM Latent Codes Through Optimal Transport
Valentin Khrulkov
Gleb Ryzhakov
Andrei Chertkov
Ivan V. Oseledets
OT
DiffM
24
51
0
14 Feb 2022
Missing Data Imputation and Acquisition with Deep Hierarchical Models
  and Hamiltonian Monte Carlo
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo
I. Peis
Chao Ma
José Miguel Hernández-Lobato
BDL
DRL
16
14
0
09 Feb 2022
Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation
Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation
F. A. Maken
Fabio Ramos
Lionel Ott
21
19
0
09 Feb 2022
Grassmann Stein Variational Gradient Descent
Grassmann Stein Variational Gradient Descent
Xingtu Liu
Harrison Zhu
Jean-François Ton
George Wynne
Andrew Duncan
23
12
0
07 Feb 2022
KSD Aggregated Goodness-of-fit Test
KSD Aggregated Goodness-of-fit Test
Antonin Schrab
Benjamin Guedj
A. Gretton
52
17
0
02 Feb 2022
Online, Informative MCMC Thinning with Kernelized Stein Discrepancy
Online, Informative MCMC Thinning with Kernelized Stein Discrepancy
Cole Hawkins
Alec Koppel
Zheng-Wei Zhang
40
4
0
18 Jan 2022
Kernel Two-Sample Tests in High Dimension: Interplay Between Moment
  Discrepancy and Dimension-and-Sample Orders
Kernel Two-Sample Tests in High Dimension: Interplay Between Moment Discrepancy and Dimension-and-Sample Orders
J. Yan
Xianyang Zhang
25
16
0
31 Dec 2021
A Generic Approach for Enhancing GANs by Regularized Latent Optimization
A Generic Approach for Enhancing GANs by Regularized Latent Optimization
Yufan Zhou
Chunyuan Li
Changyou Chen
Jinhui Xu
27
0
0
07 Dec 2021
Bounding Wasserstein distance with couplings
Bounding Wasserstein distance with couplings
N. Biswas
Lester W. Mackey
22
8
0
06 Dec 2021
Variational Wasserstein gradient flow
Variational Wasserstein gradient flow
JiaoJiao Fan
Qinsheng Zhang
Amirhossein Taghvaei
Yongxin Chen
72
54
0
04 Dec 2021
DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework
DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework
Chao Zhang
Zhijian Li
Hui Qian
Xin Du
13
10
0
02 Dec 2021
Path Integral Sampler: a stochastic control approach for sampling
Path Integral Sampler: a stochastic control approach for sampling
Qinsheng Zhang
Yongxin Chen
DiffM
18
101
0
30 Nov 2021
Density Ratio Estimation via Infinitesimal Classification
Density Ratio Estimation via Infinitesimal Classification
Kristy Choi
Chenlin Meng
Yang Song
Stefano Ermon
14
38
0
22 Nov 2021
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel
Yindong Chen
Yiwei Wang
Lulu Kang
Chun Liu
21
1
0
21 Nov 2021
Composite Goodness-of-fit Tests with Kernels
Composite Goodness-of-fit Tests with Kernels
Oscar Key
A. Gretton
F. Briol
T. Fernandez
30
14
0
19 Nov 2021
A Computationally Efficient Method for Learning Exponential Family
  Distributions
A Computationally Efficient Method for Learning Exponential Family Distributions
Abhin Shah
Devavrat Shah
G. Wornell
26
9
0
28 Oct 2021
MMD Aggregated Two-Sample Test
MMD Aggregated Two-Sample Test
Antonin Schrab
Ilmun Kim
Mélisande Albert
Béatrice Laurent
Benjamin Guedj
A. Gretton
6
55
0
28 Oct 2021
Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous
  Bias-Variance Reduction and Supercanonical Convergence
Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence
H. Lam
Haofeng Zhang
11
3
0
23 Oct 2021
On out-of-distribution detection with Bayesian neural networks
On out-of-distribution detection with Bayesian neural networks
Francesco DÁngelo
Christian Henning
BDL
UQCV
21
6
0
12 Oct 2021
Denoising Diffusion Gamma Models
Denoising Diffusion Gamma Models
Eliya Nachmani
S. Robin
Lior Wolf
DiffM
VLM
18
30
0
10 Oct 2021
A moment-matching metric for latent variable generative models
A moment-matching metric for latent variable generative models
Cédric Beaulac
14
1
0
04 Oct 2021
Generalized Kernel Thinning
Generalized Kernel Thinning
Raaz Dwivedi
Lester W. Mackey
36
29
0
04 Oct 2021
LDC-VAE: A Latent Distribution Consistency Approach to Variational
  AutoEncoders
LDC-VAE: A Latent Distribution Consistency Approach to Variational AutoEncoders
Xiaoyu Chen
Chen Gong
Qiang He
Xinwen Hou
Yu Liu
28
1
0
22 Sep 2021
Minimum Discrepancy Methods in Uncertainty Quantification
Minimum Discrepancy Methods in Uncertainty Quantification
Chris J. Oates
28
2
0
13 Sep 2021
Measuring Sample Quality in Algorithms for Intractable Normalizing
  Function Problems
Measuring Sample Quality in Algorithms for Intractable Normalizing Function Problems
Bokgyeong Kang
John Hughes
M. Haran
TPM
23
1
0
10 Sep 2021
Adversarial Stein Training for Graph Energy Models
Adversarial Stein Training for Graph Energy Models
Shiv Shankar
BDL
19
0
0
30 Aug 2021
Are Bayesian neural networks intrinsically good at out-of-distribution
  detection?
Are Bayesian neural networks intrinsically good at out-of-distribution detection?
Christian Henning
Francesco DÁngelo
Benjamin Grewe
UQCV
BDL
23
10
0
26 Jul 2021
A Survey of Monte Carlo Methods for Parameter Estimation
A Survey of Monte Carlo Methods for Parameter Estimation
D. Luengo
Luca Martino
M. Bugallo
Victor Elvira
S. Särkkä
21
153
0
25 Jul 2021
Previous
123456
Next