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Automatic Variational Inference in Stan

Automatic Variational Inference in Stan

10 June 2015
A. Kucukelbir
Rajesh Ranganath
Andrew Gelman
David M. Blei
    BDL
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Papers citing "Automatic Variational Inference in Stan"

26 / 26 papers shown
Title
Streaming Bayesian Modeling for predicting Fat-Tailed Customer Lifetime
  Value
Streaming Bayesian Modeling for predicting Fat-Tailed Customer Lifetime Value
Alexey V. Calabourdin
Konstantin A. Aksenov
20
0
0
01 Dec 2023
An Embedded Diachronic Sense Change Model with a Case Study from Ancient
  Greek
An Embedded Diachronic Sense Change Model with a Case Study from Ancient Greek
Schyan Zafar
Geoff K. Nicholls
37
1
0
01 Nov 2023
Manifold Gaussian Variational Bayes on the Precision Matrix
Manifold Gaussian Variational Bayes on the Precision Matrix
M. Magris
M. Shabani
Alexandros Iosifidis
37
2
0
26 Oct 2022
Smoothness Analysis for Probabilistic Programs with Application to
  Optimised Variational Inference
Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference
Wonyeol Lee
Xavier Rival
Hongseok Yang
21
9
0
22 Aug 2022
Modeling and mitigation of occupational safety risks in dynamic
  industrial environments
Modeling and mitigation of occupational safety risks in dynamic industrial environments
Ashutosh Tewari
António R. C. Paiva
14
1
0
02 May 2022
Simplifying deflation for non-convex optimization with applications in
  Bayesian inference and topology optimization
Simplifying deflation for non-convex optimization with applications in Bayesian inference and topology optimization
Mohamed Tarek
Yijiang Huang
19
2
0
28 Jan 2022
Epidemia: An R Package for Semi-Mechanistic Bayesian Modelling of
  Infectious Diseases using Point Processes
Epidemia: An R Package for Semi-Mechanistic Bayesian Modelling of Infectious Diseases using Point Processes
Shuhang Tan
Axel Gandy
Swapnil Mishra
Samir Bhatt
Seth Flaxman
H. Juliette T. Unwin
J. Ish-Horowicz
11
9
0
24 Oct 2021
Bayesian model selection in the $\mathcal{M}$-open setting --
  Approximate posterior inference and probability-proportional-to-size
  subsampling for efficient large-scale leave-one-out cross-validation
Bayesian model selection in the M\mathcal{M}M-open setting -- Approximate posterior inference and probability-proportional-to-size subsampling for efficient large-scale leave-one-out cross-validation
Riko Kelter
19
0
0
27 May 2020
Stochastic gradient Markov chain Monte Carlo
Stochastic gradient Markov chain Monte Carlo
Christopher Nemeth
Paul Fearnhead
BDL
22
134
0
16 Jul 2019
LF-PPL: A Low-Level First Order Probabilistic Programming Language for
  Non-Differentiable Models
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
Yuanshuo Zhou
Bradley Gram-Hansen
Tobias Kohn
Tom Rainforth
Hongseok Yang
Frank D. Wood
31
24
0
06 Mar 2019
Bayesian Incremental Learning for Deep Neural Networks
Bayesian Incremental Learning for Deep Neural Networks
Max Kochurov
T. Garipov
D. Podoprikhin
Dmitry Molchanov
Arsenii Ashukha
Dmitry Vetrov
OOD
CLL
BDL
15
22
0
20 Feb 2018
PASS-GLM: polynomial approximate sufficient statistics for scalable
  Bayesian GLM inference
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
Jonathan H. Huggins
Ryan P. Adams
Tamara Broderick
24
32
0
26 Sep 2017
Proximity Variational Inference
Proximity Variational Inference
Jaan Altosaar
Rajesh Ranganath
David M. Blei
BDL
6
21
0
24 May 2017
Adversarial Variational Bayes: Unifying Variational Autoencoders and
  Generative Adversarial Networks
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
L. Mescheder
Sebastian Nowozin
Andreas Geiger
GAN
BDL
49
525
0
17 Jan 2017
Boosting Variational Inference
Boosting Variational Inference
Fangjian Guo
Xiangyu Wang
Kai Fan
Tamara Broderick
David B. Dunson
BDL
24
75
0
17 Nov 2016
Deep Amortized Inference for Probabilistic Programs
Deep Amortized Inference for Probabilistic Programs
Daniel E. Ritchie
Paul Horsfall
Noah D. Goodman
TPM
24
81
0
18 Oct 2016
Reparameterization Gradients through Acceptance-Rejection Sampling
  Algorithms
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
C. A. Naesseth
Francisco J. R. Ruiz
Scott W. Linderman
David M. Blei
BDL
22
107
0
18 Oct 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference
  Algorithm
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Qiang Liu
Dilin Wang
BDL
19
1,067
0
16 Aug 2016
Swift: Compiled Inference for Probabilistic Programming Languages
Swift: Compiled Inference for Probabilistic Programming Languages
Yi Wu
Lei Li
Stuart J. Russell
Rastislav Bodík
23
29
0
30 Jun 2016
Automatic Variational ABC
Automatic Variational ABC
Alexander Moreno
T. Adel
Edward Meeds
James M. Rehg
Max Welling
20
12
0
28 Jun 2016
Overdispersed Black-Box Variational Inference
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
Automatic Differentiation Variational Inference
A. Kucukelbir
Dustin Tran
Rajesh Ranganath
Andrew Gelman
David M. Blei
29
708
0
02 Mar 2016
Fitting Bayesian item response models in Stata and Stan
Fitting Bayesian item response models in Stata and Stan
Robert Grant
Daniel Furr
Bob Carpenter
Andrew Gelman
59
17
0
13 Jan 2016
Variational Inference: A Review for Statisticians
Variational Inference: A Review for Statisticians
David M. Blei
A. Kucukelbir
Jon D. McAuliffe
BDL
32
4,705
0
04 Jan 2016
Black-box $α$-divergence Minimization
Black-box ααα-divergence Minimization
José Miguel Hernández-Lobato
Yingzhen Li
Mark Rowland
Daniel Hernández-Lobato
T. Bui
Richard Turner
21
137
0
10 Nov 2015
Hierarchical Variational Models
Hierarchical Variational Models
Rajesh Ranganath
Dustin Tran
David M. Blei
DRL
VLM
12
335
0
07 Nov 2015
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