Title
Stein Discrepancy for Unsupervised Domain Adaptation Anneke von Seeger Dongmian Zou Gilad Lerman 133 0 0 24 Feb 2025
On the Robustness of Kernel Goodness-of-Fit Tests Xing Liu F. Briol OOD 93 5 0 11 Aug 2024
Targeted Separation and Convergence with Kernel Discrepancies Alessandro Barp Carl-Johann Simon-Gabriel Mark Girolami Lester W. Mackey 81 15 0 26 Sep 2022
Generalised Bayesian Inference for Discrete Intractable Likelihood Takuo Matsubara Jeremias Knoblauch F. Briol Chris J. Oates 66 16 0 16 Jun 2022
Composite Goodness-of-fit Tests with Kernels Oscar Key Arthur Gretton F. Briol T. Fernandez 51 15 0 19 Nov 2021
Bounds for the chi-square approximation of Friedman's statistic by Stein's method Robert E. Gaunt Gesine Reinert 40 8 0 01 Nov 2021
Bounds for the chi-square approximation of the power divergence family of statistics Robert E. Gaunt 22 9 0 29 Jun 2021
Standardisation-function Kernel Stein Discrepancy: A Unifying View on Kernel Stein Discrepancy Tests for Goodness-of-fit Wenkai Xu 41 4 0 23 Jun 2021
Robust Generalised Bayesian Inference for Intractable Likelihoods Takuo Matsubara Jeremias Knoblauch François‐Xavier Briol Chris J. Oates UQCV 40 79 0 15 Apr 2021
A Stein Goodness of fit Test for Exponential Random Graph Models Wenkai Xu Gesine Reinert 44 5 0 28 Feb 2021
Stein Variational Gradient Descent: many-particle and long-time asymptotics Nikolas Nusken D. M. Renger 62 22 0 25 Feb 2021
Characterizations of non-normalized discrete probability distributions and their application in statistics Steffen Betsch B. Ebner F. Nestmann 52 13 0 09 Nov 2020
Measure Transport with Kernel Stein Discrepancy Matthew A. Fisher T. Nolan Matthew M. Graham D. Prangle Chris J. Oates OT 63 15 0 22 Oct 2020
Optimal quantisation of probability measures using maximum mean discrepancy Onur Teymur Jackson Gorham M. Riabiz Chris J. Oates 39 29 0 14 Oct 2020
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data T. Fernandez Nicolás Rivera Wenkai Xu Arthur Gretton 16 15 0 19 Aug 2020
Stochastic Stein Discrepancies Jackson Gorham Anant Raj Lester W. Mackey 42 37 0 06 Jul 2020
Sliced Kernelized Stein Discrepancy Wenbo Gong Yingzhen Li José Miguel Hernández-Lobato 40 37 0 30 Jun 2020
A Non-Asymptotic Analysis for Stein Variational Gradient Descent Anna Korba Adil Salim Michael Arbel Giulia Luise Arthur Gretton 49 77 0 17 Jun 2020
Scalable Control Variates for Monte Carlo Methods via Stochastic Optimization Shijing Si Chris J. Oates Andrew B. Duncan Lawrence Carin F. Briol BDL 32 21 0 12 Jun 2020
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence Sinho Chewi Thibaut Le Gouic Chen Lu Tyler Maunu Philippe Rigollet 70 69 0 03 Jun 2020
Wasserstein distance error bounds for the multivariate normal approximation of the maximum likelihood estimator Andreas Anastasiou Robert E. Gaunt 19 9 0 11 May 2020
Optimal Thinning of MCMC Output M. Riabiz W. Chen Jon Cockayne P. Swietach Steven Niederer Lester W. Mackey Chris J. Oates 41 47 0 08 May 2020
Relaxing the Gaussian assumption in Shrinkage and SURE in high dimension M. Fathi L. Goldstein Gesine Reinert Adrien Saumard 27 9 0 03 Apr 2020
On combining the zero bias transform and the empirical characteristic function to test normality B. Ebner 11 8 0 27 Feb 2020
Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration Hoang Trung-Dung Yitao Liang Guy Van den Broeck OffRL 52 3 0 25 Feb 2020
Semi-Exact Control Functionals From Sard's Method Leah F. South Toni Karvonen Christopher Nemeth Mark Girolami Chris J. Oates 20 17 0 31 Jan 2020
The reproducing Stein kernel approach for post-hoc corrected sampling Liam Hodgkinson R. Salomone Fred Roosta 47 27 0 25 Jan 2020
A new test of multivariate normality by a double estimation in a characterizing PDE Philip Dörr B. Ebner N. Henze 4 9 0 25 Nov 2019
Stein Variational Gradient Descent With Matrix-Valued Kernels Dilin Wang Ziyang Tang Minh Nguyen Qiang Liu 68 62 0 28 Oct 2019
A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family Trambak Banerjee Qiang Liu Gourab Mukherjee Wengunag Sun 50 7 0 20 Oct 2019
Variance reduction for Markov chains with application to MCMC Denis Belomestny L. Iosipoi Eric Moulines A. Naumov S. Samsonov BDL 34 30 0 08 Oct 2019
Minimum $L^q$ -distance estimators for non-normalized parametric models Steffen Betsch B. Ebner B. Klar 37 9 0 30 Aug 2019
Minimum Stein Discrepancy Estimators Alessandro Barp François‐Xavier Briol Andrew B. Duncan Mark Girolami Lester W. Mackey 43 92 0 19 Jun 2019
Stein Point Markov Chain Monte Carlo W. Chen Alessandro Barp François‐Xavier Briol Jackson Gorham Mark Girolami Lester W. Mackey Chris J. Oates 61 57 0 09 May 2019
A stochastic version of Stein Variational Gradient Descent for efficient sampling Lei Li Yingzhou Li Jian‐Guo Liu Zibu Liu Jianfeng Lu 30 35 0 09 Feb 2019
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning Yao Zhang A. Lee 28 103 0 03 Feb 2019
Testing for normality in any dimension based on a partial differential equation involving the moment generating function N. Henze J. Visagie 18 27 0 13 Jan 2019
Global Non-convex Optimization with Discretized Diffusions Murat A. Erdogdu Lester W. Mackey Ohad Shamir 43 105 0 29 Oct 2018
Stein Variational Gradient Descent as Moment Matching Qiang Liu Dilin Wang 69 38 0 27 Oct 2018
Stein Neural Sampler Tianyang Hu Zixiang Chen Hanxi Sun Jincheng Bai Mao Ye Guang Cheng SyDa GAN 34 35 0 08 Oct 2018
Random Feature Stein Discrepancies Jonathan H. Huggins Lester W. Mackey 57 45 0 20 Jun 2018
A new characterization of the Gamma distribution and associated goodness of fit tests Steffen Betsch B. Ebner 23 31 0 15 Jun 2018
Bayesian Model-Agnostic Meta-Learning Taesup Kim Jaesik Yoon Ousmane Amadou Dia Sungwoong Kim Yoshua Bengio Sungjin Ahn UQCV BDL 269 499 0 11 Jun 2018
Bounds for the asymptotic distribution of the likelihood ratio Andreas Anastasiou Gesine Reinert 17 17 0 10 Jun 2018
A Stein variational Newton method Gianluca Detommaso Tiangang Cui Alessio Spantini Youssef Marzouk Robert Scheichl 83 115 0 08 Jun 2018
Stein Variational Gradient Descent Without Gradient J. Han Qiang Liu 78 45 0 07 Jun 2018
Neural Control Variates for Variance Reduction Ruosi Wan Mingjun Zhong Haoyi Xiong Zhanxing Zhu BDL DRL 29 18 0 01 Jun 2018
A Unified Particle-Optimization Framework for Scalable Bayesian Sampling Changyou Chen Ruiyi Zhang Wenlin Wang Bai Li Liqun Chen 41 88 0 29 May 2018
Fisher Efficient Inference of Intractable Models Song Liu Takafumi Kanamori Wittawat Jitkrittum Yu Chen 49 14 0 18 May 2018
Scaling limit of the Stein variational gradient descent: the mean field regime Jianfeng Lu Yulong Lu J. Nolen 20 79 0 10 May 2018