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
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 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 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 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 Matthew Repasky Xiuyuan Cheng Yao Xie 19 3 0 07 Jul 2022
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 Shalev Shaer Yaniv Romano CML 26 2 0 03 Jul 2022
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 Antonin Schrab Ilmun Kim Benjamin Guedj A. Gretton 35 29 0 18 Jun 2022
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 George Wynne Mikolaj Kasprzak Andrew B. Duncan 25 4 0 09 Jun 2022
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 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 Wonjoon Goo S. Niekum OffRL 24 20 0 01 Jun 2022
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 Wenkai Xu Gesine Reinert SyDa 18 3 0 31 May 2022
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 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 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 Wenkai Xu Gesine Reinert 27 4 0 07 Mar 2022
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 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 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 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 F. A. Maken Fabio Ramos Lionel Ott 21 19 0 09 Feb 2022
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 Antonin Schrab Benjamin Guedj A. Gretton 52 17 0 02 Feb 2022
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 J. Yan Xianyang Zhang 25 17 0 31 Dec 2021
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 N. Biswas Lester W. Mackey 22 8 0 06 Dec 2021
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 Chao Zhang Zhijian Li Hui Qian Xin Du 13 10 0 02 Dec 2021
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 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 Yindong Chen Yiwei Wang Lulu Kang Chun Liu 21 1 0 21 Nov 2021
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 Abhin Shah Devavrat Shah G. Wornell 26 9 0 28 Oct 2021
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 H. Lam Haofeng Zhang 11 3 0 23 Oct 2021
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 Eliya Nachmani S. Robin Lior Wolf DiffM VLM 18 30 0 10 Oct 2021
A moment-matching metric for latent variable generative models Cédric Beaulac 14 1 0 04 Oct 2021
Generalized Kernel Thinning Raaz Dwivedi Lester W. Mackey 36 29 0 04 Oct 2021
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 Chris J. Oates 28 2 0 13 Sep 2021
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 Shiv Shankar BDL 19 0 0 30 Aug 2021
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 D. Luengo Luca Martino M. Bugallo Victor Elvira S. Särkkä 21 153 0 25 Jul 2021