Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors

22 June 2020

Papers citing "Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors"

39 / 39 papers shown

Title
Improving the evaluation of samplers on multi-modal targets Louis Grenioux Maxence Noble Marylou Gabrié 354 0 0 11 Apr 2025
Proximal Interacting Particle Langevin Algorithms Paula Cordero Encinar F. R. Crucinio O. Deniz Akyildiz 67 5 0 20 Jun 2024
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 67 384 0 29 Apr 2021
Adaptive Path Sampling in Metastable Posterior Distributions Yuling Yao Collin Cademartori Aki Vehtari Andrew Gelman TPM 51 6 0 01 Sep 2020
When are Bayesian model probabilities overconfident? O. Oelrich S. Ding Måns Magnusson Aki Vehtari M. Villani 46 17 0 09 Mar 2020
Holes in Bayesian Statistics Andrew Gelman Yuling Yao 41 27 0 15 Feb 2020
Automatic Reparameterisation of Probabilistic Programs Maria I. Gorinova Dave Moore Matthew D. Hoffman 38 28 0 07 Jun 2019
Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network Oscar Chang Yuling Yao David Williams-King Hod Lipson BDL UQCV 57 8 0 23 May 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 37 926 0 19 Mar 2019
Embarrassingly parallel MCMC using deep invertible transformations Diego Mesquita P. Blomstedt Samuel Kaski 36 19 0 11 Mar 2019
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap Edwin Fong Simon Lyddon Chris Holmes 128 35 0 08 Feb 2019
Uncertainty in Neural Networks: Approximately Bayesian Ensembling Tim Pearce Felix Leibfried Alexandra Brintrup Mohamed H. Zaki A. Neely BDL UQCV 57 195 0 12 Oct 2018
Measuring LDA Topic Stability from Clusters of Replicated Runs Mika Mäntylä Maëlick Claes Umar Farooq 15 49 0 24 Aug 2018
Does Hamiltonian Monte Carlo mix faster than a random walk on multimodal densities? Oren Mangoubi Natesh S. Pillai Aaron Smith 83 31 0 09 Aug 2018
Coupling and Convergence for Hamiltonian Monte Carlo Nawaf Bou-Rabee A. Eberle Raphael Zimmer 94 138 0 01 May 2018
Yes, but Did It Work?: Evaluating Variational Inference Yuling Yao Aki Vehtari Daniel P. Simpson Andrew Gelman 49 136 0 07 Feb 2018
Log-concave sampling: Metropolis-Hastings algorithms are fast Raaz Dwivedi Yuansi Chen Martin J. Wainwright Bin Yu 66 254 0 08 Jan 2018
Rapid Mixing of Hamiltonian Monte Carlo on Strongly Log-Concave Distributions Oren Mangoubi Aaron Smith 97 106 0 23 Aug 2017
Using stacking to average Bayesian predictive distributions Yuling Yao Aki Vehtari Daniel P. Simpson Andrew Gelman 68 340 0 06 Apr 2017
Deep Exploration via Randomized Value Functions Ian Osband Benjamin Van Roy Daniel Russo Zheng Wen 89 304 0 22 Mar 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 751 5,798 0 05 Dec 2016
Variational Boosting: Iteratively Refining Posterior Approximations Andrew C. Miller N. Foti Ryan P. Adams 51 125 0 20 Nov 2016
What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering) Amritanshu Agrawal Wei Fu Tim Menzies 53 215 0 29 Aug 2016
Automatic Differentiation Variational Inference A. Kucukelbir Dustin Tran Rajesh Ranganath Andrew Gelman David M. Blei 100 716 0 02 Mar 2016
Patterns of Scalable Bayesian Inference E. Angelino Matthew J. Johnson Ryan P. Adams 88 87 0 16 Feb 2016
A Bayes interpretation of stacking for M-complete and M-open settings Tri Le B. Clarke 157 39 0 16 Feb 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 246 4,778 0 04 Jan 2016
Hierarchical Variational Models Rajesh Ranganath Dustin Tran David M. Blei DRL VLM 74 338 0 07 Nov 2015
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC Aki Vehtari Andrew Gelman Jonah Gabry 106 4,044 0 16 Jul 2015
Pareto Smoothed Importance Sampling Aki Vehtari Daniel Simpson Andrew Gelman Yuling Yao Jonah Gabry 66 242 0 09 Jul 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 750 9,290 0 06 Jun 2015
Speeding Up MCMC by Efficient Data Subsampling M. Quiroz Robert Kohn M. Villani Minh-Ngoc Tran 77 175 0 16 Apr 2014
The Horseshoe Estimator: Posterior Concentration around Nearly Black Vectors S. V. D. Pas B. Kleijn A. van der Vaart 71 169 0 01 Apr 2014
Variable transformation to obtain geometric ergodicity in the random-walk Metropolis algorithm Leif Johnson C. Geyer 94 52 0 27 Feb 2013
Nonparametric variational inference S. Gershman Matt Hoffman David M. Blei BDL 104 153 0 18 Jun 2012
MCMC Methods for Functions: Modifying Old Algorithms to Make Them Faster S. Cotter Gareth O. Roberts Andrew M. Stuart D. White 97 479 0 03 Feb 2012
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 162 4,297 0 18 Nov 2011
Gaussian Process Regression with a Student-t Likelihood Pasi Jylänki J. Vanhatalo Aki Vehtari GP 93 165 0 22 Jun 2011
Philosophy and the practice of Bayesian statistics Andrew Gelman C. Shalizi 77 636 0 19 Jun 2010