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

10 February 2016

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

50 / 296 papers shown

Title
A Variational View on Bootstrap Ensembles as Bayesian Inference Dimitrios Milios Pietro Michiardi Maurizio Filippone 20 1 0 08 Jun 2020
High-Dimensional Non-Parametric Density Estimation in Mixed Smooth Sobolev Spaces Liang Ding Lu Zou Wenjia Wang Shahin Shahrampour Rui Tuo 4 3 0 05 Jun 2020
Learning Kernel Tests Without Data Splitting Jonas M. Kubler Wittawat Jitkrittum Bernhard Schölkopf Krikamol Muandet 8 22 0 03 Jun 2020
Nonparametric Score Estimators Yuhao Zhou Jiaxin Shi Jun Zhu 24 23 0 20 May 2020
Optimal Thinning of MCMC Output M. Riabiz W. Chen Jon Cockayne P. Swietach Steven Niederer Lester W. Mackey Chris J. Oates 14 45 0 08 May 2020
A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions Yulong Lu Jianfeng Lu 18 19 0 19 Apr 2020
Scalable Approximate Inference and Some Applications Jun Han BDL 22 1 0 07 Mar 2020
Batch Stationary Distribution Estimation Junfeng Wen Bo Dai Lihong Li Dale Schuurmans OffRL 14 22 0 02 Mar 2020
Stein Variational Inference for Discrete Distributions Jun Han Fan Ding Xianglong Liu Lorenzo Torresani Jian-wei Peng Qiang Liu 11 21 0 01 Mar 2020
Testing Goodness of Fit of Conditional Density Models with Kernels Wittawat Jitkrittum Heishiro Kanagawa Bernhard Schölkopf 8 27 0 24 Feb 2020
Informative Bayesian Neural Network Priors for Weak Signals Tianyu Cui A. Havulinna Pekka Marttinen Samuel Kaski 35 9 0 24 Feb 2020
Kernel Conditional Moment Test via Maximum Moment Restriction Krikamol Muandet Wittawat Jitkrittum Jonas M. Kubler 17 22 0 21 Feb 2020
A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models Ziyu Wang Shuyu Cheng Yueru Li Jun Zhu Bo Zhang 26 14 0 18 Feb 2020
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling Will Grathwohl Kuan-Chieh Jackson Wang J. Jacobsen David Duvenaud R. Zemel 13 14 0 13 Feb 2020
A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings Junhyung Park Krikamol Muandet 33 77 0 10 Feb 2020
Kullback-Leibler Divergence-Based Out-of-Distribution Detection with Flow-Based Generative Models Yufeng Zhang Jia Pan Wanwei Liu Zhenbang Chen J. Wang Zhiming Liu KenLi Li H. Wei OODD DRL 36 2 0 09 Feb 2020
Semi-Exact Control Functionals From Sard's Method Leah F. South Toni Karvonen Christopher Nemeth Mark Girolami Chris J. Oates 12 17 0 31 Jan 2020
The reproducing Stein kernel approach for post-hoc corrected sampling Liam Hodgkinson R. Salomone Fred Roosta 32 27 0 25 Jan 2020
Lifted Hybrid Variational Inference Yuqiao Chen Yibo Yang S. Natarajan Nicholas Ruozzi DRL 10 3 0 08 Jan 2020
Learning Generative Models using Denoising Density Estimators Siavash Bigdeli Geng Lin Tiziano Portenier L. A. Dunbar Matthias Zwicker DiffM 26 15 0 08 Jan 2020
Neural Density Estimation and Likelihood-free Inference George Papamakarios BDL DRL 16 44 0 29 Oct 2019
Kernel Stein Tests for Multiple Model Comparison Jen Ning Lim M. Yamada Bernhard Schölkopf Wittawat Jitkrittum 4 13 0 27 Oct 2019
Unsupervised Out-of-Distribution Detection with Batch Normalization Jiaming Song Yang Song Stefano Ermon OODD 17 22 0 21 Oct 2019
A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family Trambak Banerjee Qiang Liu Gourab Mukherjee Wengunag Sun 12 7 0 20 Oct 2019
Estimating Density Models with Truncation Boundaries using Score Matching Song Liu Takafumi Kanamori Daniel J. Williams 15 15 0 09 Oct 2019
Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators Qitian Wu Rui Gao H. Zha GAN 8 5 0 28 Sep 2019
Classification Logit Two-sample Testing by Neural Networks Xiuyuan Cheng A. Cloninger 19 31 0 25 Sep 2019
Goodness-of-fit tests on manifolds A. Shapiro Yao Xie Rui Zhang 13 5 0 11 Sep 2019
Minimum $L^q$ -distance estimators for non-normalized parametric models Steffen Betsch B. Ebner B. Klar 16 9 0 30 Aug 2019
Asymptotically Optimal One- and Two-Sample Testing with Kernels Shengyu Zhu Biao Chen Zhitang Chen Pengfei Yang 25 7 0 27 Aug 2019
Generative Modeling by Estimating Gradients of the Data Distribution Yang Song Stefano Ermon SyDa DiffM 20 3,708 0 12 Jul 2019
A Kernel Stein Test for Comparing Latent Variable Models Heishiro Kanagawa Wittawat Jitkrittum Lester W. Mackey Kenji Fukumizu A. Gretton 13 12 0 01 Jul 2019
Monte Carlo Gradient Estimation in Machine Learning S. Mohamed Mihaela Rosca Michael Figurnov A. Mnih 22 397 0 25 Jun 2019
Minimum Stein Discrepancy Estimators Alessandro Barp François‐Xavier Briol Andrew B. Duncan Mark Girolami Lester W. Mackey 30 90 0 19 Jun 2019
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond Xuechen Li Denny Wu Lester W. Mackey Murat A. Erdogdu 22 70 0 19 Jun 2019
Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality Eric T. Nalisnick Akihiro Matsukawa Yee Whye Teh Balaji Lakshminarayanan OODD 14 86 0 07 Jun 2019
Universal Boosting Variational Inference Trevor Campbell Xinglong Li 13 30 0 04 Jun 2019
Distributionally Robust Optimization and Generalization in Kernel Methods Matthew Staib Stefanie Jegelka 23 128 0 27 May 2019
A Kernel Loss for Solving the Bellman Equation Yihao Feng Lihong Li Qiang Liu 20 70 0 25 May 2019
Estimating Convergence of Markov chains with L-Lag Couplings N. Biswas Pierre E. Jacob Paul Vanetti 24 47 0 23 May 2019
Sliced Score Matching: A Scalable Approach to Density and Score Estimation Yang Song Sahaj Garg Jiaxin Shi Stefano Ermon 31 398 0 17 May 2019
Stein Point Markov Chain Monte Carlo W. Chen Alessandro Barp François‐Xavier Briol Jackson Gorham Mark Girolami Lester W. Mackey Chris J. Oates 30 56 0 09 May 2019
A Normality Test for High-dimensional Data based on a Nearest Neighbor Approach Hao Chen Yin Xia 18 7 0 10 Apr 2019
Kernelized Complete Conditional Stein Discrepancy Raghav Singhal Xintian Han S. Lahlou Rajesh Ranganath 19 7 0 09 Apr 2019
The Born Supremacy: Quantum Advantage and Training of an Ising Born Machine Brian Coyle Daniel Mills V. Danos E. Kashefi 24 155 0 03 Apr 2019
Functional Variational Bayesian Neural Networks Shengyang Sun Guodong Zhang Jiaxin Shi Roger C. Grosse BDL 17 235 0 14 Mar 2019
Stochastic Gradient MCMC for Nonlinear State Space Models Christopher Aicher Srshti Putcha Christopher Nemeth Paul Fearnhead E. Fox BDL 20 7 0 29 Jan 2019
Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights Melanie F. Pradier Weiwei Pan Jiayu Yao S. Ghosh Finale Doshi-velez UQCV BDL 6 9 0 16 Nov 2018
Stein Variational Gradient Descent as Moment Matching Qiang Liu Dilin Wang 12 37 0 27 Oct 2018
Informative Features for Model Comparison Wittawat Jitkrittum Heishiro Kanagawa Patsorn Sangkloy James Hays Bernhard Schölkopf A. Gretton 17 27 0 27 Oct 2018