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Variational Inference: A Review for Statisticians
v1v2v3v4v5v6v7v8v9 (latest)

Variational Inference: A Review for Statisticians

4 January 2016
David M. Blei
A. Kucukelbir
Jon D. McAuliffe
    BDL
ArXiv (abs)PDFHTML

Papers citing "Variational Inference: A Review for Statisticians"

50 / 1,838 papers shown
Title
Conditionally structured variational Gaussian approximation with
  importance weights
Conditionally structured variational Gaussian approximation with importance weights
Linda S. L. Tan
Aishwarya Bhaskaran
David J. Nott
125
13
0
21 Apr 2019
Design of Communication Systems using Deep Learning: A Variational
  Inference Perspective
Design of Communication Systems using Deep Learning: A Variational Inference Perspective
Vishnu Raj
Sheetal Kalyani
94
23
0
18 Apr 2019
An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning
An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning
Yaoyao Liu
Bernt Schiele
Qianru Sun
BDL
146
128
0
17 Apr 2019
High-dimensional copula variational approximation through transformation
High-dimensional copula variational approximation through transformation
M. Smith
Rubén Loaiza-Maya
David J. Nott
79
33
0
16 Apr 2019
Copula-like Variational Inference
Copula-like Variational Inference
Marcel Hirt
P. Dellaportas
Alain Durmus
42
5
0
15 Apr 2019
Variational Bayes for high-dimensional linear regression with sparse
  priors
Variational Bayes for high-dimensional linear regression with sparse priors
Kolyan Ray
Botond Szabó
82
99
0
15 Apr 2019
Connections Between Adaptive Control and Optimization in Machine
  Learning
Connections Between Adaptive Control and Optimization in Machine Learning
Joseph E. Gaudio
T. Gibson
Anuradha M. Annaswamy
M. Bolender
E. Lavretsky
AI4CE
29
44
0
11 Apr 2019
Know Your Boundaries: Constraining Gaussian Processes by Variational
  Harmonic Features
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
Arno Solin
Manon Kok
74
23
0
10 Apr 2019
Learning Attribute Patterns in High-Dimensional Structured Latent
  Attribute Models
Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models
Yuqi Gu
Gongjun Xu
63
24
0
08 Apr 2019
Bayesian Estimation of Mixed Multinomial Logit Models: Advances and
  Simulation-Based Evaluations
Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations
P. Bansal
Rico Krueger
M. Bierlaire
Ricardo A. Daziano
T. Rashidi
31
32
0
07 Apr 2019
Combining Sentiment Lexica with a Multi-View Variational Autoencoder
Combining Sentiment Lexica with a Multi-View Variational Autoencoder
Alexander Miserlis Hoyle
Lawrence Wolf-Sonkin
Hanna M. Wallach
Ryan Cotterell
Isabelle Augenstein
50
9
0
05 Apr 2019
A deterministic and computable Bernstein-von Mises theorem
A deterministic and computable Bernstein-von Mises theorem
Guillaume P. Dehaene
72
14
0
04 Apr 2019
dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian
  Posteriors and Evidences
dynesty: A Dynamic Nested Sampling Package for Estimating Bayesian Posteriors and Evidences
J. Speagle
88
1,223
0
03 Apr 2019
The Medical Deconfounder: Assessing Treatment Effects with Electronic
  Health Records
The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records
Linying Zhang
Yixin Wang
A. Ostropolets
J. J. Mulgrave
David M. Blei
G. Hripcsak
BDLCML
136
1
0
03 Apr 2019
Identification, Interpretability, and Bayesian Word Embeddings
Identification, Interpretability, and Bayesian Word Embeddings
Adam M. Lauretig
BDL
42
11
0
02 Apr 2019
Unsupervised Contextual Anomaly Detection using Joint Deep Variational
  Generative Models
Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Models
Yaniv Shulman
DRL
28
6
0
01 Apr 2019
Informed Machine Learning -- A Taxonomy and Survey of Integrating
  Knowledge into Learning Systems
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
Laura von Rueden
S. Mayer
Katharina Beckh
B. Georgiev
Sven Giesselbach
...
Rajkumar Ramamurthy
Michal Walczak
Jochen Garcke
Christian Bauckhage
Jannis Schuecker
157
653
0
29 Mar 2019
Variational Bayesian modelling of mixed-effects
Variational Bayesian modelling of mixed-effects
J. Daunizeau
8
4
0
21 Mar 2019
Performance Measurement for Deep Bayesian Neural Network
Yikuan Li
Yajie Zhu
BDLOODUQCV
27
1
0
20 Mar 2019
A semi-supervised deep learning algorithm for abnormal EEG
  identification
A semi-supervised deep learning algorithm for abnormal EEG identification
Subhrajit Roy
Kiran Kate
Martin Hirzel
24
3
0
19 Mar 2019
Pairwise Comparisons with Flexible Time-Dynamics
Pairwise Comparisons with Flexible Time-Dynamics
Lucas Maystre
Victor Kristof
Matthias Grossglauser
SyDa
25
0
0
18 Mar 2019
Approximating exponential family models (not single distributions) with
  a two-network architecture
Approximating exponential family models (not single distributions) with a two-network architecture
Sean R. Bittner
John P. Cunningham
46
4
0
18 Mar 2019
What You Say and How You Say it: Joint Modeling of Topics and Discourse
  in Microblog Conversations
What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
Jichuan Zeng
Jing Li
Yulan He
Cuiyun Gao
Michael R. Lyu
Irwin King
BDL
45
27
0
18 Mar 2019
GEE: A Gradient-based Explainable Variational Autoencoder for Network
  Anomaly Detection
GEE: A Gradient-based Explainable Variational Autoencoder for Network Anomaly Detection
Q. Nguyen
Kar Wai Lim
D. Divakaran
K. H. Low
M. Chan
DRL
68
137
0
15 Mar 2019
Deep Switch Networks for Generating Discrete Data and Language
Deep Switch Networks for Generating Discrete Data and Language
Payam Delgosha
Naveen Goela
23
0
0
14 Mar 2019
Learning Latent Representations of Bank Customers With The Variational
  Autoencoder
Learning Latent Representations of Bank Customers With The Variational Autoencoder
R. A. Mancisidor
Michael C. Kampffmeyer
K. Aas
Robert Jenssen
DRLBDL
33
25
0
14 Mar 2019
Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy
Augment-Reinforce-Merge Policy Gradient for Binary Stochastic Policy
Yunhao Tang
Mingzhang Yin
Mingyuan Zhou
21
0
0
13 Mar 2019
On the Statistical Consistency of Risk-Sensitive Bayesian
  Decision-Making
On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making
Prateek Jaiswal
Harsha Honnappa
Vinayak A. Rao
40
2
0
12 Mar 2019
Elements of Sequential Monte Carlo
Elements of Sequential Monte Carlo
C. A. Naesseth
Fredrik Lindsten
Thomas B. Schon
75
97
0
12 Mar 2019
A cross-center smoothness prior for variational Bayesian brain tissue
  segmentation
A cross-center smoothness prior for variational Bayesian brain tissue segmentation
Wouter M. Kouw
S. Ørting
Jens Petersen
K. S. Pedersen
Marleen de Bruijne
65
8
0
11 Mar 2019
$β^3$-IRT: A New Item Response Model and its Applications
β3β^3β3-IRT: A New Item Response Model and its Applications
Yu Chen
Telmo de Menezes e Silva Filho
R. Prudêncio
Tom Diethe
Peter A. Flach
144
30
0
10 Mar 2019
Imputation estimators for unnormalized models with missing data
Imputation estimators for unnormalized models with missing data
Masatoshi Uehara
Takeru Matsuda
Jae Kwang Kim
37
7
0
08 Mar 2019
Based on Graph-VAE Model to Predict Student's Score
Based on Graph-VAE Model to Predict Student's Score
Yang Zhang
Ming-choa Lu
13
0
0
08 Mar 2019
Streamlined Computing for Variational Inference with Higher Level Random
  Effects
Streamlined Computing for Variational Inference with Higher Level Random Effects
T. Nolan
M. Menictas
M. Wand
32
14
0
07 Mar 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 Wood
80
25
0
06 Mar 2019
Probabilistic Modeling for Novelty Detection with Applications to Fraud
  Identification
Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification
Rémi Domingues
AAML
40
3
0
05 Mar 2019
Empirical priors for prediction in sparse high-dimensional linear
  regression
Empirical priors for prediction in sparse high-dimensional linear regression
Ryan Martin
Yiqi Tang
211
21
0
03 Mar 2019
Approximation Properties of Variational Bayes for Vector Autoregressions
Approximation Properties of Variational Bayes for Vector Autoregressions
Reza Hajargasht
BDL
26
1
0
02 Mar 2019
The principles of adaptation in organisms and machines I: machine
  learning, information theory, and thermodynamics
The principles of adaptation in organisms and machines I: machine learning, information theory, and thermodynamics
Hideaki Shimazaki
DRLAI4CE
19
6
0
28 Feb 2019
Variational Inference to Measure Model Uncertainty in Deep Neural
  Networks
Variational Inference to Measure Model Uncertainty in Deep Neural Networks
K. Posch
J. Steinbrener
J. Pilz
UQCVBDL
58
29
0
26 Feb 2019
Banded Matrix Operators for Gaussian Markov Models in the Automatic
  Differentiation Era
Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era
N. Durrande
Vincent Adam
L. Bordeaux
Stefanos Eleftheriadis
J. Hensman
76
26
0
26 Feb 2019
A Nonparametric Multi-view Model for Estimating Cell Type-Specific Gene
  Regulatory Networks
A Nonparametric Multi-view Model for Estimating Cell Type-Specific Gene Regulatory Networks
Cassandra Burdziak
E. Azizi
Sandhya Prabhakaran
Dana Peér
26
14
0
21 Feb 2019
Multifidelity Bayesian Optimization for Binomial Output
Multifidelity Bayesian Optimization for Binomial Output
L. Matyushin
Alexey Zaytsev
O. Alenkin
Andrey Ustuzhanin
24
0
0
19 Feb 2019
Divergence-Based Motivation for Online EM and Combining Hidden Variable
  Models
Divergence-Based Motivation for Online EM and Combining Hidden Variable Models
Ehsan Amid
Manfred K. Warmuth
57
4
0
11 Feb 2019
A physics-aware, probabilistic machine learning framework for
  coarse-graining high-dimensional systems in the Small Data regime
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime
Constantin Grigo
P. Koutsourelakis
AI4CE
131
26
0
11 Feb 2019
Estimating the Rate Constant from Biosensor Data via an Adaptive
  Variational Bayesian Approach
Estimating the Rate Constant from Biosensor Data via an Adaptive Variational Bayesian Approach
Y. Zhang
Z. Yao
P. Forssén
T. Fornstedt
25
5
0
11 Feb 2019
Manifold Optimization Assisted Gaussian Variational Approximation
Manifold Optimization Assisted Gaussian Variational Approximation
Bingxin Zhou
Junbin Gao
Minh-Ngoc Tran
Richard Gerlach
67
6
0
11 Feb 2019
A stochastic version of Stein Variational Gradient Descent for efficient
  sampling
A stochastic version of Stein Variational Gradient Descent for efficient sampling
Lei Li
Yingzhou Li
Jian‐Guo Liu
Zibu Liu
Jianfeng Lu
63
35
0
09 Feb 2019
Scalable Nonparametric Sampling from Multimodal Posteriors with the
  Posterior Bootstrap
Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap
Edwin Fong
Simon Lyddon
Chris Holmes
184
36
0
08 Feb 2019
Meta-Amortized Variational Inference and Learning
Meta-Amortized Variational Inference and Learning
Mike Wu
Kristy Choi
Noah D. Goodman
Stefano Ermon
OODVLMBDLDRL
84
36
0
05 Feb 2019
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