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Learning Proposals for Probabilistic Programs with Inference Combinators

1 March 2021

Jan-Willem van de Meent

NAI

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Papers citing "Learning Proposals for Probabilistic Programs with Inference Combinators"

40 / 40 papers shown

Title
Nested Variational Inference Heiko Zimmermann Hao Wu Babak Esmaeili Jan-Willem van de Meent BDL 70 21 0 21 Jun 2021
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 500 10,591 0 17 Feb 2020
Amortized Population Gibbs Samplers with Neural Sufficient Statistics Hao Wu Heiko Zimmermann Eli Sennesh T. Le Jan-Willem van de Meent 34 7 0 04 Nov 2019
Functional Tensors for Probabilistic Programming F. Obermeyer Eli Bingham M. Jankowiak Du Phan Jonathan P. Chen 42 18 0 23 Oct 2019
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale A. G. Baydin Lei Shao W. Bhimji Lukas Heinrich Lawrence Meadows ... Philip Torr Victor W. Lee Kyle Cranmer P. Prabhat Frank Wood 69 58 0 08 Jul 2019
The Thermodynamic Variational Objective Vaden Masrani T. Le Frank Wood 71 48 0 28 Jun 2019
Elements of Sequential Monte Carlo C. A. Naesseth Fredrik Lindsten Thomas B. Schon 73 97 0 12 Mar 2019
Multi-Object Representation Learning with Iterative Variational Inference Klaus Greff Raphael Lopez Kaufman Rishabh Kabra Nicholas Watters Christopher P. Burgess Daniel Zoran Loic Matthey M. Botvinick Alexander Lerchner OCL SSL 106 509 0 01 Mar 2019
Simple, Distributed, and Accelerated Probabilistic Programming Like Hui Matthew Hoffman Siyuan Ma Christopher Suter Srinivas Vasudevan Alexey Radul M. Belkin Rif A. Saurous BDL 61 56 0 05 Nov 2018
Pyro: Deep Universal Probabilistic Programming Eli Bingham Jonathan P. Chen M. Jankowiak F. Obermeyer Neeraj Pradhan Theofanis Karaletsos Rohit Singh Paul A. Szerlip Paul Horsfall Noah D. Goodman BDL GP 158 1,057 0 18 Oct 2018
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives George Tucker Dieterich Lawson S. Gu Chris J. Maddison BDL 69 110 0 09 Oct 2018
Automated learning with a probabilistic programming language: Birch Lawrence M. Murray Thomas B. Schon 50 63 0 02 Oct 2018
An Introduction to Probabilistic Programming Jan-Willem van de Meent Brooks Paige Hongseok Yang Frank Wood GP 73 200 0 27 Sep 2018
Improving Explorability in Variational Inference with Annealed Variational Objectives Chin-Wei Huang Shawn Tan Alexandre Lacoste Aaron Courville DRL 44 47 0 06 Sep 2018
Hamiltonian Variational Auto-Encoder Anthony L. Caterini Arnaud Doucet Dino Sejdinovic BDL DRL 57 95 0 29 May 2018
Tighter Variational Bounds are Not Necessarily Better Tom Rainforth Adam R. Kosiorek T. Le Chris J. Maddison Maximilian Igl Frank Wood Yee Whye Teh DRL 172 198 0 13 Feb 2018
Learning Disentangled Representations with Semi-Supervised Deep Generative Models Siddharth Narayanaswamy Brooks Paige Jan-Willem van de Meent Alban Desmaison Noah D. Goodman Pushmeet Kohli Frank Wood Philip Torr DRL CoGe 133 363 0 01 Jun 2017
Variational Sequential Monte Carlo C. A. Naesseth Scott W. Linderman Rajesh Ranganath David M. Blei BDL 263 214 0 31 May 2017
Auto-Encoding Sequential Monte Carlo T. Le Maximilian Igl Tom Rainforth Tom Jin Frank Wood BDL DRL 309 152 0 29 May 2017
Filtering Variational Objectives Chris J. Maddison Dieterich Lawson George Tucker N. Heess Mohammad Norouzi A. Mnih Arnaud Doucet Yee Whye Teh FedML 238 210 0 25 May 2017
Grammar Variational Autoencoder Matt J. Kusner Brooks Paige José Miguel Hernández-Lobato BDL DRL 85 844 0 06 Mar 2017
Approximate Inference with Amortised MCMC Yingzhen Li Richard Turner Qiang Liu BDL 66 62 0 27 Feb 2017
A Convenient Category for Higher-Order Probability Theory C. Heunen Ohad Kammar S. Staton Hongseok Yang 58 161 0 10 Jan 2017
Edward: A library for probabilistic modeling, inference, and criticism Dustin Tran A. Kucukelbir Adji Bousso Dieng Maja R. Rudolph Dawen Liang David M. Blei 78 300 0 31 Oct 2016
Deep Amortized Inference for Probabilistic Programs Daniel E. Ritchie Paul Horsfall Noah D. Goodman TPM 103 82 0 18 Oct 2016
Composing inference algorithms as program transformations R. Zinkov Chung-chieh Shan TPM 42 30 0 06 Mar 2016
Importance Weighted Autoencoders Yuri Burda Roger C. Grosse Ruslan Salakhutdinov BDL 276 1,246 0 01 Sep 2015
Black-Box Policy Search with Probabilistic Programs Jan-Willem van de Meent Brooks Paige David Tolpin Frank Wood 48 24 0 16 Jul 2015
A New Approach to Probabilistic Programming Inference Frank Wood Jan-Willem van de Meent Vikash K. Mansinghka 61 347 0 03 Jul 2015
Gradient Estimation Using Stochastic Computation Graphs John Schulman N. Heess T. Weber Pieter Abbeel OffRL 148 395 0 17 Jun 2015
Nested Sequential Monte Carlo Methods C. A. Naesseth Fredrik Lindsten Thomas B. Schon 433 84 0 09 Feb 2015
Reweighted Wake-Sleep J. Bornschein Yoshua Bengio BDL 109 183 0 11 Jun 2014
Venture: a higher-order probabilistic programming platform with programmable inference Vikash K. Mansinghka Daniel Selsam Yura N. Perov 71 256 0 01 Apr 2014
A Compilation Target for Probabilistic Programming Languages Brooks Paige Frank Wood 75 80 0 03 Mar 2014
Black Box Variational Inference Rajesh Ranganath S. Gerrish David M. Blei DRL BDL 150 1,167 0 31 Dec 2013
Measure Transformer Semantics for Bayesian Machine Learning J. Borgström Andrew D. Gordon Michael Greenberg J. Margetson Jurgen Van Gael 79 104 0 03 Aug 2013
Bayesian State-Space Modelling on High-Performance Hardware Using LibBi Lawrence M. Murray 88 113 0 14 Jun 2013
Parallel resampling in the particle filter Lawrence M. Murray Anthony Lee Pierre E. Jacob 70 139 0 17 Jan 2013
Automated Variational Inference in Probabilistic Programming David Wingate T. Weber BDL TPM 91 139 0 07 Jan 2013
Church: a language for generative models Noah D. Goodman Vikash K. Mansinghka Daniel M. Roy Keith Bonawitz J. Tenenbaum 107 819 0 13 Jun 2012