Awesome Variational Inference

QQ Group: 849170086

Tutorial and Review

• Advances in Variational Inference (2018) Cheng Zhang, Judith Bütepage, Hedvig Kjellström, Stephan Mandt. [arXiv] T-PAMI [IEEExplore]

• Variational Inference: A Review for Statisticians (2017) David Meir Blei, Alp Kucukelbir, Jon D McAuliffe. [arXiv]

Stochastic VI

• Stochastic Variational Inference (2013) Matthew D Hoffman, David Meir Blei, Chong Wang, John Paisley. JMLR, http://www.columbia.edu/~jwp2128/Papers/HoffmanBleiWangPaisley2013.pdf

• Online Learning for Latent Dirichlet Allocation (2010) Matthew D Hoffman, David Meir Blei, Francis Bach. [NIPS]

• Online Variational Bayesian Learning (2003) Antti Honkela, Harri Valpola. Bulletin of the Italian Artificial Intelligence Association

• Online Variational Inference for the Hierarchical Dirichlet Process (2011) Chong Wang, John William Paisley, David Meir Blei. AISTATS

• Online Model Selection Based on the Variational Bayes (2001) Masa-aki Sato. Neural Computation

• Variational Message Passing with Structured Inference Networks (2018) Wu Lin, Nicolas Hubacher, Mohammad Emtiyaz Khan. [arXiv]

• Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam (2018) Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava. [arXiv]

• The Population Posterior and Bayesian Modeling on Streams (2015) James McInerney, Rajesh Ranganath, David Meir Blei. [NIPS]

Mean Field VI

• A Mean Field Theory Learning Algorithm for Neural Networks (1987) Carsten Peterson, James R Anderson. Complex Systems

• Improving the Mean Field Approximation via the Use of Mixture Distributions (1998) Tommi S Jaakkola, Michael I Jordan. NATO ASI series D Behaviroural and Social Sciences. https://link.springer.com/chapter/10.1007%2F978-94-011-5014-9_6

• Graphical Models, Exponential Families, and Variational Inference (2008) Martin J Wainwright, Michael I Jordan. Foundations and Trends in Machine Learning https://dl.acm.org/doi/10.1561/2200000001

• An Introduction to Variational Methods for Graphical Models (1999) Michael I Jordan, Zoubin Ghahramani, Tommi S Jaakkola, Lawrence K Saul. Machine Learning https://link.springer.com/article/10.1023/A:1007665907178

• Stochastic Gradient Variational Bayes for Gamma Approximating Distributions (2015) David A Knowles. [arXiv]

• Variational Message Passing (2005) Christopher M Bishop. JMLR http://www.johnwinn.org/Publications/papers/VMP2004.pdf

• Mean Field Theory for Sigmoid Belief Networks (1996) Lawrence K Saul, Tommi S Jaakkola, Michael I Jordan. [arXiv] Journal of Artificial Intelligence Research

• Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models (2013) Manfred Opper, Ulrich Paquet, Ole Winther. JMLR

• A Mean Field Algorithm for Bayes Learning in Large Feed-Forward Neural Networks (1996) Manfred Opper, Ole Winther. [NIPS]

• Expectation Propagation (2014) Jack Raymond, Andre Manoel, Manfred Opper. [arXiv]

• Estimation of Third-order Correlations within Mean Field Approximation (1998) Toshiyuki Tanaka. NIPS

• A Theory of Mean Field Approximation (1999) Toshiyuki Tanaka. NIPS

• Information Geometry of Mean-Field Approximation (2000) Toshiyuki Tanaka. Neural Computation

SVI with Natural Gradient

• Approximate Riemannian Conjugate Gradient Learning for Fixed-form Variational Bayes (2010) Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, Juha Karhunen. JMLR

• Natural Conjugate Gradient in Variational Inference (2007) Antti Honkela, Matti Tornio, Tapani Raiko, Juha Karhunen. ICONIP.

Probabilistic Graphical Models

• Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks (1996) Tommi S Jaakkola, Lawrence K Saul, Michael I Jordan. [NIPS]

Learning Rate and Batch Size

• An Adaptive Learning Rate for Stochastic Variational Inference (2013) Rajesh Ranganath, Chong Wang, David Meir Blei, Eric P. Xing. ICML. http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=03755599E78362132A98CF3D3EC611E5?doi=10.1.1.298.1299&rep=rep1&type=pdf

• Automated Inference with Adaptive Batches (2017) Soham De, Abhay Yadav, David Jacobs, Tom Goldstein. AISTATS. https://sohamde.github.io/docs/aistats17_paper.pdf

• Stochastic Variational Inference for Largescale Discrete Choice Models using Adaptive Batch Sizes (2017) Linda SL Tan. Statistics and Computing

Variance Reduction

• Accelerating Stochastic Gradient Descent using Predictive Variance Reduction (2013) Rie Johnson, Tong Zhang. [NIPS])

• REBAR: Low-variance, Unbiased Gradient Estimates for Discrete Latent Variable Models (2017) George Tucker, Andriy Mnih, Chris J Maddison, Dieterich Lawson, Jascha Sohldickstein. [arXiv] NIPS.

Variance-bias Trade-off

• Tighter Variational Bounds are not Necessarily Better (2018) Tom Rainforth, Adam R Kosiorek, Tuan Anh Le, Chris J Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh. [arXiv] ICML.

Collapsed VI

• Fast Variational Inference in the Conjugate Exponential Family (2012) James Hensman, Magnus Rattray, Neil D Lawrence. NIPS.

• Fast Variational Inference for Gaussian Process Models through KL-correction (2006) Nathaniel J King, Neil D Lawrence. ECML.

• Collapsed Variational Dirichlet Process Mixture Models (2007) Kenichi Kurihara, Max Welling, Yee Whye Teh. IJCAI.

• Overlapping Mixtures of Gaussian Processes for the Data Association Problem (2012) Miguel Lázaro-Gredilla, Steven Van Vaerenbergh, Neil D Lawrence. [arXiv] Pattern Recognition

• Latent-space Variational Bayes (2008) Jaemo Sung, Zoubin Ghahramani, Sungyang Bang. TPAMI

• A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation (2006) Yee Whye Teh, David Newman, Max Welling. [NIPS]

• Variational Heteroscedastic Gaussian Process Regression (2011) Michalis K Titsias, Miguel L Zarogredilla. ICML.

Collapsed VI for Topic Model

• Neural Variational Inference for Text Processing (2016) Yishu Miao, Lei Yu, Phil Blunsom. [arXiv] ICML

• Autoencoding Variational Inference for Topic Models (2017) Akash Srivastava, Charles Sutton. [arXiv] ICLR.

Sparse Inference

• Variational Learning of Inducing Variables in Sparse Gaussian Processes (2009) Michalis K Titsias. AISTATS.

• Gaussian Processes for Big Data (2013) James Hensman, Nicolo Fusi, Neil D Lawrence. [arXiv] UAI

• Deep Gaussian Processes (2013) Andreas Damianou, Neil D Lawrence. AISTATS

• Sparse Bayesian Learning and the Relevance Vector Machine (2001) Michael E Tippingg J. Machine Learning Research

• Sparse Bayesian Learning for Basis Selection (2004) David P Wipf, Bhaskar D Rao TSP

• A Unified Bayesian Inference Framework for Generalized Linear Models (2018) Xiangming Meng, Sheng Wu, Jiang Zhu. [arXiv] IEEE Signal Processing Letters

Line Spectral Estimation

• Variational Bayesian Line Spectral Estimation with Multiple Measurement Vectors (2019) Jiang Zhu, Qi Zhang, Peter Gerstoft, Mihai-Alin Badiu, Zhiwei Xu. [arXiv]

• Grid-less Variational Bayesian Channel Estimation for Antenna Array Systems with Low Resolution ADCs (2019) Jiang Zhu, Chaokai Wen, Jun Tong, Chongbin Xu, Shi Jin. [arXiv] IEEE Trans. Wireless Communications.

• Gridless Variational Line Spectral Estimation with Multiple Measurement Vector from Quantized Samples (2019) Jiang Zhu, Qi Zhang, Benzhou Jin, Zhiwei Xu. [arXiv]

• Grid-less Variational Direction of Arrival Estimation in Heteroscedastic Noise Environment (2019) Qi Zhang, Jiang Zhu, Yuantao Gu, Zhiwei Xu. [arXiv]

Laplace Method

• Accurate Approximations for Posterior Moments and Marginal Densities (1986) Luke Tierney, Joseph B Kadane. Journal of the American Statistical Association

• Laplace's Method Approximations for Probabilistic Inferencein Belief Networks with Continuous Variables (1994) Adriano Azevedo-Filho, Ross D Shachter. [arXiv] UAI

• Variational Inference in Nonconjugate Models (2013) Chong Wang, David Meir Blei. JMLR

Black-box VI

• Black Box Variational Inference (2014) Rajesh Ranganath, Sean Gerrish, David Meir Blei. AISTATS.

• Local Expectation Gradients for Black Box Variational Inference (2015) Michalis Titsias RC AUEB, Miguel LázaroGredilla. NIPS.

• Smoothed Gradients for Stochastic Variational Inference (2014) Stephan Mandt, David Meir Blei. [arXiv] NIPS

• Variational Bayesian Inference with Stochastic Search (2012) David Meir Blei, Michael I Jordan, John Paisley. ICML

• Overdispersed Black-box Variational Inference (2016) Francisco J R Ruiz, Michalis K Titsias, David Meir Blei. [arXiv] UAI

• Perturbative Black Box Variational Inference (2017) Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt. [arXiv] NIPS

Reparameterized-based VI

• Auto-encoding Variational Bayes (2014) Diederik P Kingma, Max Welling. [arXiv] ICLR

• Stochastic Backpropagation and Approximate Inference in Deep Generative Models (2014) Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra. [arXiv] ICML

• Sticking the Landing: An Asymptotically Zero-variance Gradient Estimator for Variational Inference (2017) Geoffrey Roeder, Yuhuai Wu, David Duvenaud. [arXiv] NIPS

• Categorical Reparameterization with Gumbel-softmax (2017) Eric Jang, Shixiang Gu, Ben Poole. ICLR

• The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables (2017) Chris J Maddison, Andriy Mnih, Yee Whye Teh. [arXiv]

• The Generalized Reparameterization Gradient (2016) Francisco J R Ruiz, Michalis K Titsias, David Meir Blei. [NIPS]

• Reparameterization Gradients Through Acceptance-rejection Sampling Algorithms (2017) Christian A Naesseth, Francisco J R Ruiz, Scott W Linderman, David Meir Blei. [arXiv] AISTATS

• Reducing Reparameterization Gradient Variance (2017) Andrew C Miller, Nicholas J Foti, Alexander D'Amour, Ryan P Adams. [arXiv] NIPS

• Quasi-Monte Carlo Variational Inference (2018) Alexander Buchholz, Florian Wenzel, Stephan Mandt. [arXiv] ICML

General VI Model

Approximation with Inner Optimization

• Correlated Topic Models (2006) David Meir Blei, John D. Lafferty. NIPS http://dl.acm.org/citation.cfm?id=2976267

• Simultaneous Image Classification and Annotation (2009) Chong Wang, David Meir Blei, Fei-Fei Li. CVPR http://doi.ieeecomputersociety.org/10.1109/CVPRW.2009.5206800

• Supervised Hierarchical Dirichlet Processes with Variational Inference (2013) Cheng Zhang, Carl Henrik Ek, Xavi Gratal, Florian T Pokorny, Hedvig Kjellstrom. ICCV WS https://www.computer.org/csdl/proceedings-article/iccvw/2013/3022a254/12OmNCesr26

Lower Bound with Close-Form Update

• Variational Inference in Nonconjugate Models (2013) Chong Wang, David Meir Blei. JMLR

• Non-conjugate Variational Message Passing for Multinomial and Binary Regression (2011) David A Knowles, Tom P Minka. [NIPS]

Linear Regression

• Fixed-form Variational Posterior Approximation through Stochastic Linear Regression (2013) Tim Salimans, David Knowles. [arXiv] Bayesian Analysis

Split Model

• Kullback-Leibler Proximal Variational Inference (2015) Mohammademtiyaz Emtiyaz Khan, Pierre Baqué, François Fleuret, Pascal Fua. NIPS http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.734.1922&rep=rep1&type=pdf

Neural Networks

• Variational Bayes Solution of Linear Neural Networks and its Generalization Performance (2007) Shinichi Nakajima, Sumio Watanabe. Neural Computation

• Deep Variational Information Bottleneck (2017) Alexander A Alemi, Ian Fischer, Joshua V Dillon, Kevin Murphy. [arXiv] ICLR

• Bayesian Learning for Neural Networks (2012) Geoffrey E Hinton, Radford M Neal. Springer Science & Business Media

Alternative Divergence Measure

• Renyi Divergence Variational Inference (2016) Yingzhen Li, Richard E Turner. [NIPS]

• Black-Box α-divergence Minimization (2016) Jose Miguel Hernandezlobato, Yingzhen Li, Mark Rowland, Daniel Hernandezlobato, Thang D Bui, Richard E Turner. [arXiv] ICML

• Variational Inference via χ-upper Bound Minimization (2017) Adji B Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, David Meir Blei. [arXiv] NIPS

• Divergence Measures and Message Passing (2005) Tom P Minka. Microsoft Research Technical Report

• Perturbative Black Box Variational Inference (2017) Robert Bamler, Cheng Zhang, Manfred Opper, Stephan Mandt. [arXiv] NIPS

• Perturbation Theory for Variational Inference (2015) Manfred Opper, Marco Fraccaro, Ulrich Paquet, lex Susemihl, Ole Winther. NIPS WS http://approximateinference.org/accepted/OpperEtAl2015.pdf

• Online Spike-and-slab Inference with Stochastic Expectation Propagation (2016) Shandian Zhe, Kuang-chih Lee, Kai Zhang, Jennifer Neville. NIPS WS http://www.cs.utah.edu/~zhe/pdf/sepss.pdf

• Stein Variational Adaptive Importance Sampling (2017) Jun Han, Qiang Liu. [arXiv] UAI

• A Kernelized Stein Discrepancy for Goodness-of-fit Tests (2016) Qiang Liu, Jason D Lee, Michael I Jordan. ICML.

• Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm (2016) Qiang Liu, Dilin Wang. [arXiv] NIPS

• Stein Variational Policy Gradient (2017) Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng. [arXiv] UAI

• Operator Variational Inference (2016) Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David Meir Blei. NIPS [arXiv]

• Variational Inference with Normalizing Flows (2015) Danilo Jimenez Rezende, Shakir Mohamed. [arXiv] ICML

Structured VI

• Structured Stochastic Variational Inference (2015) Matthew D Hoffman, David Meir Blei. [arXiv] AISTATS

• An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process (2015) Amar Shah, David A Knowles, Zoubin Ghahramani. ICML

Hierarchical VI

• Hierarchical Variational models (2016) Rajesh Ranganath, Dustin Tran, David Meir Blei. [arXiv] ICML

• The Variational Gaussian Process (2016) Dustin Tran, Rajesh Ranganath, David Meir Blei. [arXiv] stat

Copula VI

• Variational Gaussian Copula Inference (2016) Shaobo Han, Xuejun Liao, David B Dunson, Lawrence Carin. [arXiv]

• Copula Variational Inference (2015) Dustin Tran, David Meir Blei, Edoardo M Airoldi. [arXiv] NIPS

Times series

• Structured Black Box Variational Inference for Latent Time Series Models (2017) Robert Bamler, Stephan Mandt. [arXiv] ICML WS

• Stochastic Variational Inference for Hidden Markov Models (2014) Nicholas J Foti, Jason Xu, Dillon Laird, Emily B Fox. [arXiv] NIPS

• Stochastic Variational Inference for Bayesian Time Series Models (2014) Matthew J Johnson, Alan S Willsky. ICML

Mixture Distribution

• Improving the Mean Field Approximation via the Use of Mixture Distributions (1998) Tommi S Jaakkola, Michael I Jordon. NATO ASI series D behaviroural and social sciences

• Efficient Gradient-Free Variational Inference using Policy Search (2018) Oleg Arenz, Mingjun Zhong, Gerhard Neumann. ICML

• Nonparametric Variational Inference (2012) Samuel J Gershman, Matthew D Hoffman, David Meir Blei. ICML

• Boosting Variational Inference (2016) Fangjian Guo, Xiangyu Wang, Kai Fan, Tamara Broderick, David B Dunson. [arXiv]

• Variational Boosting: Iteratively Refining Posterior Approximations (2017) Andrew Miller, Nicholas J Foti, Ryan P Adams. ICML

Stochastic Gradient Descent

• A Variational Analysis of Stochastic Gradient Algorithms (2016) Stephan Mandt, Matthew D Hoffman, David Meir Blei. ICML [arXiv]

• Stochastic Gradient Descent as Approximate Bayesian Inference (2017) Stephan Mandt, Matthew D Hoffman, David Meir Blei. JMLR

• Adaptive Subgradient Methods for Online Learning and Stochastic Optimization (2011) John C Duchi, Elad Hazan, Yoram Singer. JMLR

• Bayesian Learning via Stochastic Gradient Langevin Dynamics (2011) Max Welling, Yee Whye Teh. ICML.

• Early Stopping as Nonparametric Variational Inference (2016) David Duvenaud, Dougal Maclaurin, Ryan P Adams. AISTATS

Variational Temper

• Variational Tempering (2016) Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David Meir Blei. [arXiv] AISTATS

• Probabilistic Inference using Markov Chain Monte Carlo Methods (1993) Radford M Neal. Technical Report http://www.utstat.utoronto.ca/~radford/sta4503.S13/review.pdf

• A Deterministic Annealing Approach to Clustering (1990) Kenneth Rose, E Gurewwitz, Geoffrey C Fox. Pattern Recognition Letters

Robust Optimization

• Robust Probabilistic Modeling with Bayesian Data Reweighting (2017) Yixin Wang, Alp Kucukelbir, David Meir Blei. ICML [arXiv]

• A Trust-region Method for Stochastic Variational Inference with Applications to Streaming Data (2015) Matthew D Hoffman. ICML [arXiv]

• Population Empirical Bayes (2015) Alp Kucukelbir, David Meir Blei. [arXiv] UAI

Amortized VI

• Variational Auto-encoded Deep Gaussian Processes (2016) Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil D Lawrence. [arXiv] ICLR

• Deep Gaussian Processes (2013) Andreas Damianou, Neil D Lawrence. AISTATS

• Iterative Amortized Inference (2018) Joseph Marino, Yisong Yue, and Stephan Mandt. ICML [arXiv]

Variational Auto Encoder

• Stochastic Gradient VB and the Variational Auto-encoder (2014) Diederik P Kingma, Max Welling. [arXiv] ICLR

• Auto-encoding Variational Bayes (2014) Diederik P Kingma, Max Welling. [arXiv] ICLR

• Stochastic Backpropagation and Approximate Inference in Deep Generative Models (2014) Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra. [arXiv] ICML

• Discrete Variational Autoencoders (2017) Jason Tyler Rolfe. [arXiv] ICLR

• Inference Suboptimality in Variational Autoencoders (2018) Chris Cremer, Xuechen Li, David Duvenaud. [arXiv] ICML

• Stick-breaking Variational Autoencoders (2017) Eric Nalisnick, Padhraic Smyth. [arXiv]

• Variational Inference using Implicit Distributions (2017) Ferenc Huszár. [arXiv]

• Variational Lossy Autoencoder (2017) Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel. [arXiv] ICLR

• Debiasing Evidence Approximations: On Importance-weighted Autoencoders and Jackknife Variational Inference (2018) Sebastian Nowozin. ICLR https://openreview.net/forum?id=HyZoi-WRb

• Structured VAEs: Composing Probabilistic Graphical Models and Variational Autoencoders (2016) Matthew J Johnson, David Duvenaud, Alexander B Wiltschko, Sandeep Robert Datta, Ryan P Adams. [arXiv] NIPS

• Draw: A Recurrent Neural Network for Image Generation (2015) Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra. [arXiv] ICML

• How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks (2016) Casper Kaae Sønderby, Tapani Raiko, Lars Maaloe, Soren Kaae Sønderby, Ole Winther. ICML.

• Factorized Variational Autoencoders for Modeling Audience Reactions to Movies (2017) Zhiwei Deng, Rajitha Navarathna, Peter Carr, Stephan Mandt, Yisong Yue, Iain Matthews, Greg Mori. CVPR

• Structured VAEs: Composing Probabilistic Graphical Models and Variational Autoencoders (2016) Matthew J Johnson, David Duvenaud, Alexander B Wiltschko, Sandeep Robert Datta, Ryan P Adams. [arXiv] NIPS

• Improving Variational Autoencoders with Inverse Autoregressive Flow (2016) Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling. NIPS

Implicit Distribution

• Variational Inference using Implicit Distributions (2017) Ferenc Huszar. [arXiv]

• Adversarial Message Passing for Graphical Models (2016) Theofanis Karaletsos. [arxiv] NIPS WS.

• Wild Variational Approximations (2016) Yingzhen Li, Qiang Liu NIPS WS http://approximateinference.org/accepted/LiLiu2016.pdf

• Approximate Inference with Amortised MCMC (2017) Yingzhen Li, Richard E Turner, Qiang Liu. [arXiv]

• Two Methods for Wild Variational Inference (2016) Qiang Liu, Yihao Feng. [arXiv]

• Fast and Scalable Bayesian Deep Learning by Weight-perturbation in Adam (2018) Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava. [arXiv]

• Learning in Implicit Generative Models (2016) Shakir Mohamed, Balaji Lakshminarayanan. [arXiv]

• Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC Distributions (2017) Michalis K Titsias. [arXiv]

• Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning (2016) Dilin Wang, Qiang Liu. [arXiv]

• Hierarchical Implicit Models and Likelihood-free Variational Inference (2017) Dustin Tran, Rajesh Ranganath, David Meir Blei. [arXiv]

• Semi-implicit Variational Inference (2018) Mingzhang Yin, Mingyuan Zhou. [arXiv] ICML

• Importance Weighted Autoencoders (2015) Yuri Burda, Roger Grosse, Ruslan Salakhutdinov. [arXiv]

• Reinterpreting Importance-weighted Autoencoders (2017) Chris Cremer, Quaid Morris, David Duvenaud. ICLR WS. https://openreview.net/pdf?id=Syw2ZgrFx

Normalizing Flow

• Variational Inference with Normalizing Flows (2015) Danilo Jimenez Rezende, Shakir Mohamed. [arXiv] ICML.

• Improving Variational Autoencoders with Inverse Autoregressive Flow (2016) Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling. NIPS.

Approximate Message Passing

• Vector Approximate Message Passing (2016) S. Rangan, P. Schniter, A. Fletcher [arXiv]

• Vector Approximate Message Passing for the Generalized Linear Model (2016) P Schniter, S. Rangan, A. Fletcher

• An Expectation Propagation Perspective on Approximate Message Passing (2015) Xiangming Meng, Sheng Wu, Linling Kuang, J Lu. IEEE Signal Processing Letters

• Bilinear Adaptive Generalized Vector Approximation Message Passing (2018) Xiangming Meng, Jiang Zhu. IEEE Access

• An AMP-Based Low Complexity Generalized Sparse Bayesian Learning Algorithm (2018) Jiang Zhu, Lin Han, Xiangming Meng. IEEE Access

• Vector Approximate Message Passing Algorithm for Structured Perturbed Sensing Matrix (2018) Jiang Zhu, Qi Zhang, Xiangming Meng, Zhiwei Xu. [arXiv]

• Vector Approximate Message Passing Algorithm for Compressed Sensing with Structured Matrix Perturbation (2020) Jiang Zhu, Qi Zhang, Xiangming Meng, Zhiwei Xu. Signal Processing

Parallel and Distributed Inference

• Streaming Variational Bayes (2013) Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C Wilson, Michael I Jordan. [NIPS]

• Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models (2014) Yarin Gal, Mark Van Der Wilk, Carl Edward Rasmussen. [arXiv] NIPS

• Parallelized Variational EM for Latent Dirichlet Allocation: An Experimental Evaluation of Speed and Scalability (2007) Ramesh Nallapati, William W Cohen, John Lafferty. ICDM WS

• Embarrassingly Parallel Variational Inference in Nonconjugate Models (2015) Willie Neiswanger, Chong Wang, Eric P Xing. [arXiv]

• Mr. LDA: A Flexible Large Scale Topic Modeling Package using Variational Inference in MapReduce (2012) Ke Zhai, Jordan Boyd-Graber, Nima Asadi, M. L. Alkhouja. WWW https://dl.acm.org/doi/10.1145/2187836.2187955

Reinforcement Learning

• Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review (2018) Sergey Levine. [arXiv]

• Variational Policy Search via Trajectory Optimization (2013) Sergey Levine, Vladlen Koltun. [NIPS]

• Stein Variational Policy Gradient (2017) Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng. UAI [arXiv]

• Variational Policy for Guiding Point Processes (2017) ichen Wang, Grady Williams, Evangelos A Theodorou, Le Song. ICML [arXiv]

• Simple Statistical Gradient-following Algorithms for Connectionist Reinforcement Learning (1992) Ronald J Williams. Machine Learning

Others

• Approximate Inference for the Loss-calibrated Bayesian (2011) Simon Lacoste-Julien, Ferenc Huszar, Zoubin Ghahramani. AISTATS.

• Frequentist Consistency of Variational Bayes (2018) Yixin Wang, David Meir Blei.

• Information Maximization in Noisy Channels : A Variational Approach (2003) David Barber, Felix Agakov. [NIPS]

Contributions to this repo are welcome.