v1v2 (latest)

Yes, but Did It Work?: Evaluating Variational Inference

7 February 2018

Papers citing "Yes, but Did It Work?: Evaluating Variational Inference"

40 / 40 papers shown

Title
STiL: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classification Siyi Du Xinzhe Luo D. O’Regan Chen Qin 123 0 0 08 Mar 2025
Variational Bayesian Bow tie Neural Networks with Shrinkage Alisa Sheinkman Sara Wade BDL UQCV 100 0 0 17 Nov 2024
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models Jinlin Lai Justin Domke Daniel Sheldon 128 0 0 31 Oct 2024
Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix C. Margossian Lawrence K. Saul 92 2 0 14 Oct 2024
Amortized Bayesian Multilevel Models Daniel Habermann Marvin Schmitt Lars Kühmichel Andreas Bulling Stefan T. Radev Paul-Christian Bürkner 252 4 0 23 Aug 2024
SoftCVI: Contrastive variational inference with self-generated soft labels Daniel Ward Mark Beaumont Matteo Fasiolo BDL 233 1 0 22 Jul 2024
Flexible Tails for Normalizing Flows Tennessee Hickling Dennis Prangle 59 0 0 22 Jun 2024
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs C. Margossian Loucas Pillaud-Vivien Lawrence K. Saul UD 158 2 0 20 Mar 2024
A Heavy-Tailed Algebra for Probabilistic Programming Feynman T. Liang Liam Hodgkinson Michael W. Mahoney 67 3 0 15 Jun 2023
On the Convergence of Black-Box Variational Inference Kyurae Kim Jisu Oh Kaiwen Wu Yi-An Ma Jacob R. Gardner BDL 94 17 0 24 May 2023
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images Matthew D. Hoffman T. Le Pavel Sountsov Christopher Suter Ben Lee Vikash K. Mansinghka Rif A. Saurous BDL 75 14 0 27 Oct 2022
A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms Paul-Christian Bürkner Ilja Kroker S. Oladyshkin Wolfgang Nowak 70 10 0 12 Apr 2022
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference Luca Rendsburg Agustinus Kristiadi Philipp Hennig U. V. Luxburg 67 2 0 07 Mar 2022
Approximating Bayes in the 21st Century G. Martin David T. Frazier Christian P. Robert 77 26 0 20 Dec 2021
Validating Gaussian Process Models with Simulation-Based Calibration John Mcleod F. Simpson 61 3 0 27 Oct 2021
Variational inference for cutting feedback in misspecified models Xue Yu David J. Nott M. Smith 73 14 0 25 Aug 2021
Pathfinder: Parallel quasi-Newton variational inference Lu Zhang Bob Carpenter A. Gelman Aki Vehtari 134 41 0 09 Aug 2021
q-Paths: Generalizing the Geometric Annealing Path using Power Means Vaden Masrani Rob Brekelmans T. Bui Frank Nielsen Aram Galstyan Greg Ver Steeg Frank Wood 264 16 0 01 Jul 2021
Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence Ghassen Jerfel S. Wang Clara Fannjiang Katherine A. Heller Yi-An Ma Michael I. Jordan BDL 129 40 0 30 Jun 2021
Monotonic Alpha-divergence Minimisation for Variational Inference Kamélia Daudel Randal Douc François Roueff 88 9 0 09 Mar 2021
An Easy to Interpret Diagnostic for Approximate Inference: Symmetric Divergence Over Simulations Justin Domke 22 9 0 25 Feb 2021
Robust, Accurate Stochastic Optimization for Variational Inference Akash Kumar Dhaka Alejandro Catalina Michael Riis Andersen Maans Magnusson Jonathan H. Huggins Aki Vehtari 71 34 0 01 Sep 2020
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors Yuling Yao Aki Vehtari Andrew Gelman 98 63 0 22 Jun 2020
Distortion estimates for approximate Bayesian inference Hanwen Xing Geoff K. Nicholls J. Lee 33 7 0 19 Jun 2020
Variational Bayesian Monte Carlo with Noisy Likelihoods Luigi Acerbi 102 42 0 15 Jun 2020
Bayesian Neural Network via Stochastic Gradient Descent Abhinav Sagar UQCV BDL 58 2 0 04 Jun 2020
The role of exchangeability in causal inference O. Saarela D. Stephens E. Moodie 112 7 0 02 Jun 2020
Assessment and adjustment of approximate inference algorithms using the law of total variance Xue Yu David J. Nott Minh-Ngoc Tran Nadja Klein 63 15 0 20 Nov 2019
Validated Variational Inference via Practical Posterior Error Bounds Jonathan H. Huggins Mikolaj Kasprzak Trevor Campbell Tamara Broderick 90 37 0 09 Oct 2019
Provable Gradient Variance Guarantees for Black-Box Variational Inference Justin Domke DRL 59 23 0 19 Jun 2019
Automatic Reparameterisation of Probabilistic Programs Maria I. Gorinova Dave Moore Matthew D. Hoffman 71 29 0 07 Jun 2019
Universal Boosting Variational Inference Trevor Campbell Xinglong Li 59 32 0 04 Jun 2019
Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network Oscar Chang Yuling Yao David Williams-King Hod Lipson BDL UQCV 71 8 0 23 May 2019
A Contrastive Divergence for Combining Variational Inference and MCMC Francisco J. R. Ruiz Michalis K. Titsias BDL 69 60 0 10 May 2019
Bayesian leave-one-out cross-validation for large data Måns Magnusson Michael Riis Andersen J. Jonasson Aki Vehtari 111 26 0 24 Apr 2019
Provable Smoothness Guarantees for Black-Box Variational Inference Justin Domke 74 36 0 24 Jan 2019
Posterior inference unchained with EL_2O U. Seljak Byeonghee Yu 54 10 0 14 Jan 2019
Calibration procedures for approximate Bayesian credible sets J. Lee Geoff K. Nicholls Robin J. Ryder 55 13 0 15 Oct 2018
Variational Bayesian Monte Carlo Luigi Acerbi BDL 68 66 0 12 Oct 2018
Pareto Smoothed Importance Sampling Aki Vehtari Daniel Simpson Andrew Gelman Yuling Yao Jonah Gabry 145 242 0 09 Jul 2015