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ELBOing Stein: Variational Bayes with Stein Mixture Inference

30 October 2024

Papers citing "ELBOing Stein: Variational Bayes with Stein Mixture Inference"

40 / 40 papers shown

Title
Reparameterization invariance in approximate Bayesian inference Hrittik Roy M. Miani Carl Henrik Ek Philipp Hennig Marvin Pfortner Lukas Tatzel Søren Hauberg BDL 124 9 0 05 Jun 2024
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference Dai Hai Nguyen Tetsuya Sakurai Hiroshi Mamitsuka 139 2 0 25 Oct 2023
Particle-based Variational Inference with Preconditioned Functional Gradient Flow Hanze Dong Xi Wang Yong Lin Tong Zhang 100 20 0 25 Nov 2022
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent Jiaxin Shi Lester W. Mackey 83 20 0 17 Nov 2022
Repulsive Deep Ensembles are Bayesian Francesco DÁngelo Vincent Fortuin UQCV BDL 129 101 0 22 Jun 2021
Bayesian Deep Learning via Subnetwork Inference Erik A. Daxberger Eric T. Nalisnick J. Allingham Javier Antorán José Miguel Hernández-Lobato UQCV BDL 130 86 0 28 Oct 2020
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence Sinho Chewi Thibaut Le Gouic Chen Lu Tyler Maunu Philippe Rigollet 100 70 0 03 Jun 2020
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro Du Phan Neeraj Pradhan M. Jankowiak 66 359 0 24 Dec 2019
Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables Yaniv Yacoby Weiwei Pan Finale Doshi-Velez BDL UQCV 70 1 0 01 Nov 2019
Stein Variational Gradient Descent With Matrix-Valued Kernels Dilin Wang Ziyang Tang Minh Nguyen Qiang Liu 90 62 0 28 Oct 2019
Ín-Between' Uncertainty in Bayesian Neural Networks Andrew Y. K. Foong Yingzhen Li José Miguel Hernández-Lobato Richard Turner BDL UQCV 73 121 0 27 Jun 2019
$Rank-normalization, folding, and localization: An improved $\widehat{R}$ for assessing convergence of MCMC$ Rank-normalization, folding, and localization: An improved $\widehat{R}$ for assessing convergence of MCMC Aki Vehtari Andrew Gelman Daniel P. Simpson Bob Carpenter Paul-Christian Bürkner 107 954 0 19 Mar 2019
Stochastic Gradient MCMC with Repulsive Forces Víctor Gallego D. Insua BDL 70 37 0 30 Nov 2018
On the Importance of Strong Baselines in Bayesian Deep Learning Jishnu Mukhoti Pontus Stenetorp Y. Gal UQCV BDL 71 33 0 23 Nov 2018
Stein Variational Gradient Descent as Moment Matching Qiang Liu Dilin Wang 104 38 0 27 Oct 2018
A Stein variational Newton method Gianluca Detommaso Tiangang Cui Alessio Spantini Youssef Marzouk Robert Scheichl 143 117 0 08 Jun 2018
A Unified Particle-Optimization Framework for Scalable Bayesian Sampling Changyou Chen Ruiyi Zhang Wenlin Wang Bai Li Liqun Chen 69 89 0 29 May 2018
Scaling limit of the Stein variational gradient descent: the mean field regime Jianfeng Lu Yulong Lu J. Nolen 85 80 0 10 May 2018
Deep Learning using Rectified Linear Units (ReLU) Abien Fred Agarap 122 3,252 0 22 Mar 2018
Riemannian Stein Variational Gradient Descent for Bayesian Inference Chang-rui Liu Jun Zhu 65 67 0 30 Nov 2017
Message Passing Stein Variational Gradient Descent Jingwei Zhuo Chang-rui Liu Jiaxin Shi Jun Zhu Ning Chen Bo Zhang 77 92 0 13 Nov 2017
Learning to Draw Samples with Amortized Stein Variational Gradient Descent Yihao Feng Dilin Wang Qiang Liu GAN BDL 85 82 0 20 Jul 2017
A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI Justin Domke BDL 80 6 0 20 Jun 2017
Stein Variational Gradient Descent as Gradient Flow Qiang Liu OT 137 277 0 25 Apr 2017
Stein Variational Adaptive Importance Sampling J. Han Qiang Liu 150 28 0 18 Apr 2017
Measuring Sample Quality with Kernels Jackson Gorham Lester W. Mackey 189 224 0 06 Mar 2017
Variational Boosting: Iteratively Refining Posterior Approximations Andrew C. Miller N. Foti Ryan P. Adams 92 124 0 20 Nov 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 136 1,095 0 16 Aug 2016
Automatic Differentiation Variational Inference A. Kucukelbir Dustin Tran Rajesh Ranganath Andrew Gelman David M. Blei 133 720 0 02 Mar 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 381 4,832 0 04 Jan 2016
Hierarchical Variational Models Rajesh Ranganath Dustin Tran David M. Blei DRL VLM 111 337 0 07 Nov 2015
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC Aki Vehtari Andrew Gelman Jonah Gabry 164 4,095 0 16 Jul 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks José Miguel Hernández-Lobato Ryan P. Adams UQCV BDL 153 945 0 18 Feb 2015
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images Anh Totti Nguyen J. Yosinski Jeff Clune AAML 192 3,276 0 05 Dec 2014
Variational Particle Approximations A. Saeedi Tejas D. Kulkarni Vikash K. Mansinghka S. Gershman 186 60 0 24 Feb 2014
Black Box Variational Inference Rajesh Ranganath S. Gerrish David M. Blei DRL BDL 205 1,167 0 31 Dec 2013
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 312 14,988 1 21 Dec 2013
Stochastic Variational Inference Matt Hoffman David M. Blei Chong-Jun Wang John Paisley BDL 291 2,631 0 29 Jun 2012
Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression Tim Salimans David A. Knowles 136 251 0 28 Jun 2012
Nonparametric variational inference S. Gershman Matt Hoffman David M. Blei BDL 117 154 0 18 Jun 2012