Automated Variational Inference in Probabilistic Programming

7 January 2013

Papers citing "Automated Variational Inference in Probabilistic Programming"

24 / 24 papers shown

Title
Provable convergence guarantees for black-box variational inference Justin Domke Guillaume Garrigos Robert Mansel Gower 18 18 0 04 Jun 2023
Dr. Neurosymbolic, or: How I Learned to Stop Worrying and Accept Statistics Masataro Asai 25 0 0 08 Sep 2022
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference Wonyeol Lee Xavier Rival Hongseok Yang 24 9 0 22 Aug 2022
TyXe: Pyro-based Bayesian neural nets for Pytorch H. Ritter Theofanis Karaletsos OOD MU BDL 28 6 0 01 Oct 2021
Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection H. Akrami Anand A. Joshi Sergul Aydore Richard M. Leahy UQCV 39 4 0 20 Sep 2021
Nested Variational Inference Heiko Zimmermann Hao Wu Babak Esmaeili Jan-Willem van de Meent BDL 27 20 0 21 Jun 2021
Semi-parametric $γ$ -ray modeling with Gaussian processes and variational inference S. Mishra-Sharma Kyle Cranmer MedIm 21 7 0 20 Oct 2020
CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19 Zhaozhi Qian Ahmed Alaa Mihaela van der Schaar 34 41 0 27 Jul 2020
Stochastically Differentiable Probabilistic Programs David Tolpin Yuanshuo Zhou Hongseok Yang BDL 11 0 0 02 Mar 2020
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support Yuanshuo Zhou Hongseok Yang Yee Whye Teh Tom Rainforth TPM 29 19 0 29 Oct 2019
Monte Carlo Gradient Estimation in Machine Learning S. Mohamed Mihaela Rosca Michael Figurnov A. Mnih 45 397 0 25 Jun 2019
Effect Handling for Composable Program Transformations in Edward2 Dave Moore Maria I. Gorinova 6 15 0 15 Nov 2018
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model A. G. Baydin Lukas Heinrich W. Bhimji Lei Shao Saeid Naderiparizi ... Philip Torr Victor W. Lee P. Prabhat Kyle Cranmer Frank Wood 26 31 0 20 Jul 2018
Pathwise Derivatives Beyond the Reparameterization Trick M. Jankowiak F. Obermeyer 30 110 0 05 Jun 2018
Reparameterization Gradient for Non-differentiable Models Wonyeol Lee Hangyeol Yu Hongseok Yang DRL 19 30 0 01 Jun 2018
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models George Tucker A. Mnih Chris J. Maddison John Lawson Jascha Narain Sohl-Dickstein BDL 47 282 0 21 Mar 2017
Deep Amortized Inference for Probabilistic Programs Daniel E. Ritchie Paul Horsfall Noah D. Goodman TPM 24 81 0 18 Oct 2016
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks Daniel E. Ritchie Anna T. Thomas Pat Hanrahan Noah D. Goodman TPM 28 11 0 19 Mar 2016
Overdispersed Black-Box Variational Inference Francisco J. R. Ruiz Michalis K. Titsias David M. Blei 22 47 0 03 Mar 2016
Automatic Differentiation Variational Inference A. Kucukelbir Dustin Tran Rajesh Ranganath Andrew Gelman David M. Blei 38 709 0 02 Mar 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 38 4,710 0 04 Jan 2016
Gradient Estimation Using Stochastic Computation Graphs John Schulman N. Heess T. Weber Pieter Abbeel OffRL 19 389 0 17 Jun 2015
Automatic Variational Inference in Stan A. Kucukelbir Rajesh Ranganath Andrew Gelman David M. Blei BDL 29 232 0 10 Jun 2015
Variational Inference with Normalizing Flows Danilo Jimenez Rezende S. Mohamed DRL BDL 23 4,105 0 21 May 2015