The collection of literature work on Probabilistic Graphical Models (PGMs). Source file can be found at git repository pgm-map.
- Kingma and Welling, 2019, An Introduction to Variational Autoencoders
- D. Barber, 2012, Bayesian Reasoning and Machine Learning
- Roger D. Peng, Advanced Statistical Computing (in progress)
- Sutton, 2010, An Introduction to Conditional Random Fields
- Wainwright, 2008, Graphical Models, Exponential Families, and Variational Inference
- Koller, 2009, Probabilistic graphical models: principles and techniques
- Mark Rowland, 2018, Structure in Machine Learning: Graphical Models and Monte Carlo Methods
- Yingzhen Li, 2018, Approximate Inference: New Visions
- Adrian Weller, 2014, Methods for Inference in Graphical Models
- Angelino, et al 2016, Patterns of Scalable Bayesian Inference
- Komodakis etc, 2016, (Hyper)-Graphs Inference through Convex Relaxations and Move Making Algorithms: Contributions and Applications in Artificial Vision
- Bogdan Savchynskyy, 2019, Discrete Graphical Models – An Optimization Perspective
- Angelino, 2016, Patterns of Scalable Bayesian Inference
- Nowozin, 2011, Structured Learning and Prediction in Computer Vision
- Dieng, Adji Bousso, 2020, Deep Probabilistic Graphical Modeling
- Lou, Qi, 2018, Anytime Approximate Inference in Graphical Models
- Ping, Wei, 2016, Learning and Inference in Latent Variable Graphical Models
- Forouzan, Sholeh, 2015, Approximate Inference in Graphical Models
- Qiang, Liu, 2014, Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework
- Yuan Qi, 2005, Extending Expectation Propagation for Graphical Models
- Thomas P Minka, 2001, A family of algorithms for approximate Bayesian inference
- Dieng, Adji Bousso, 2020, Deep Probabilistic Graphical Modeling
- Lee et al, 2019, EMP, Convergence rates of smooth message passing with rounding in entropy-regularized MAP inference
- Knoll, et al, 2018, Fixed Points of Belief Propagation – An Analysis via Polynomial Homotopy Continuation
- Cheng Zhag, et al, 2018, Advances in Variational Inference
- Peters, Janzing, Scholkopf, 2017, Elements of Causal Inference.
- Fletcher, 2017, Expectation Consistent Approximate Inference: Generalizations and Convergence
- Donoho, et al 2010, Message Passing Algorithms for Compressed Sensing: I. Motivation and Construction
- Donoho, et al 2010, Message passing algorithms for compressed sensing: II. analysis and validation
- Convergence Analysis, Roosta, 2008, Convergence Analysis of Reweighted Sum-Product Algorithms
- Generalized BP for marginal distributions, Yedidis, et al, 2005, Constructing free energy approximations and Generalized belief propagation algorithms
- Tree-structured EP, Minka and Qi, Tree-structured approximations by expectation propagation
- Winn & Bishop, 2005, Variational message passing
- Welling, Minka, Teh, 2005, Structured Region Graphs: Morphing EP into GBP
- Max Welling, 2004, On the Choice of Regions for Generalized Belief Propagation
- Opper, Winther, 2005, Expectation Consistent Approximate Inference
- Wainwright et al, 2003, tree-reweighted belief propagation algorithms and approximated ML esimation by pseudo-moment matching
- Wainwright and Willsky, 2003, Exact MAP estimates by hypertree agreement
- Tourani et al, 2018, MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models
- Haller et al, 2018, Exact MAP-Inference by Confining Combinatorial Search with LP Relaxation
- Globerson, Jaakkola, 2008, Fixing Max-Product: Convergent Message PassingAlgorithms for MAP LP-Relaxations
- Conditioning, Clamping, Divide
- Zhou et al, 2020, Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
- Eaton and Ghahramani, 2009, Choosing a Variable to Clamp
- Geier et al, 2015, Locally Conditioned Belief Propagation
- Weller and Jebara, 2014, Clamping Variables and Approximate Inference
- Nate Derbinsky, José Bento, Veit Elser, Jonathan S. Yedidia, An Improved Three-Weight Message-Passing Algorithm, slide
- Linear Response. Welling and Teh, Linear Response Algorithms for Approximate Inference in Graphical Models
- Combining with Particle/Stochastic Methods
- Liu et al, 2015, Probabilistic Variational Bounds for Graphical Models
- Noorshams and Wainwright, 2013, stochastic belief propagation: a low-complexity alternative to the sum-product algorithm
- Lienart, et al, Expectation Particle Belief Propagation
- Ihler, McAllester, 2009, Particle Belief Propagation
- Sudderth, Nonparametric Belief Propagation
- Mixture/multi-modal
- Baque et al, 2017, Multi-Modal Mean-Fields via Cardinality-Based Clamping
- Hao Xiong et al, 2019, One-Shot Marginal MAP Inference in Markov Random Fields
- Layered messages
- Jampani et al, 2015, Consensus Message Passing for Layered Graphical Models
- Patrick Eschenfeldt, Dan Schmidt, Stark Draper, Jonathan Yedidia, 2016, Patrick Eschenfeldt, Dan Schmidt, Stark Draper, Jonathan Yedidia
- Satorras, 2019, Combining Generative and Discriminative Models for Hybrid Inference
- Zheng, 2019, Conditional Random Fields as Recurrent Neural Networks
- Krahenbuhl, 2011, Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
- NIPS tutorial 2016, Variational Inference
- Kingma and Welling, 2014, Autoencoder: Auto-Encoding Variational Bayes
- Kuleshov and Ermon, 2017, NVIL: Neural Variational Inference and Learning in Undirected Graphical Models
- Li, etc, 2020, AdVIL: To Relieve Your Headache of Training an MRF, Take AdVIL
- Lazaro-Gredilla, 2019 (Vicarious AI), Learning undirected models via query training
- Sobolev and Vetrov, 2019, (Section 3 gives interesting discussion on literature works) Importance Weighted Hierarchical Variational Inference
- Kingma, et al, 2016, Improved Variational Inference with Inverse Autoregressive Flow
- Rezende, Mohamed, 2015, Variational Inference with Normalizing Flows
- Domke, 2019, Provable Smoothness Guarantees for Black-Box Variational Inference
- Zhang, et al, 2018, Advances in Variational Inference
- Blei, 2017, Variational Inference: A Review for Statisticians
- Regier et al, 2017, Fast Black-box Variational Inferencethrough Stochastic Trust-Region Optimization
- Kucukelbir et al, 2016, Automatic differentiation variational inference
- Black-box alpha, 2016, Black-box alpha-divergence minimization
- Ranganath et al, 2014, Black box variational inference
- Stoller et al, 2020, Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators
- Karaletsos, 2016, Adversarial Message Passing For Graphical Models
- Yiming Yan et al, 2019, Amortized Inference of Variational Bounds for Learning Noisy-OR
Learning messages
- Heess et al, Learning to Pass Expectation Propagation Messages, half-automated message passing, message-level automation
- Kuck et al 2020, Belief Propagation Neural Networks
- Victor Garcia Satorras, Max Welling, 2020 Neural Enhanced Belief Propagation on Factor Graphs
- Yoon et al, 2018, Inference in Probabilistic Graphical Models by Graph Neural Networks
- Lin, 2015, Deeply Learning the Messages in Message Passing Inference
Graphical Neural Networks
- GMNN: Graph Markov Neural Networks, semi-supervised learning, EM is used for training.
- More generalized computation power: Graph Net Library, A graph network takes a graph as input and returns a graph as output.
- Related, Deep Graph Library, for deep learning on graphs
- Scarselli et al, 2009, The graph neural network model
- Satorras and Welling, 2020, Neural Enhanced Belief Propagation on Factor Graphs
- Chen et al, 2018, ODE: Neural Ordinary Differential Equations
- Kingma, Dhariwal, 2018, Glow: Generative Flow with Invertible 1x1 Convolutions
- Dinh, Sohl-Dickstein, Bengio, 2017, Density Estimation using Real NVP
- Dinh, Krueger, Bengio, 2014, NICE: Non-linear independent component estimation
- Tran, 2019, Discrete flows: Invertible generative models of discrete data
- Inverse autoregreeeive flow as in previous subsection.
Alternative objective
- Note, Maximum Pseudolikelihood Learning
- Domke, 2013, Learning Graphical Model Parameters with Approximate Marginal Inference
Learning graphical model parameters by approximate inference
- Tang, 2015, Bethe Learning of Conditional Random Fields via MAP Decoding
- You Lu, 2019, Block Belief Propagation for Parameter Learning in Markov Random Fields
- Hazan, 2016, Blending Learning and Inference in Conditional Random Fields
- Tang, etc, 2016, Bethe Learning of Graphical Models via MAP Decoding
- Ping and Ihler, 2017, Belief Propagation in Conditional RBMs for Structured Prediction
- Ping, et al, 2014, Marginal Structured SVM with Hidden Variables
Learning of MRF with neural networks
- Wiseman and Kim, 2019, Amortized Bethe Free Energy Minimization for Learning MRFs
- Kuleshov and Ermon, 2017, Neural Variational Inference and Learning in Undirected Graphical Models
- Lazaro-Gredilla et al, 2020, Query Training: Learning and inference for directed and undirected graphical models
Learning of Directed Graphs
- Chongxuan Li, 2020, To Relieve Your Headache of Training an MRF, Take AdVIL
- Mnih and Gregor, 2014, Neural Variational Inference and Learning in Belief Networks
- NIPS tutorial 2016, Variational Inference
- Kim, Ahn, Bengio, 2019, Variational Temporal Abstraction
- Yulia Rubanova et al 2019, Latent ODEs for Irregularly-Sampled Time Series
- Linderman et al, 2017, Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems
- Niall Twomey, Michal Kozlowski, Raul Santos-Rodriguez, 2020, Neural ODEs with stochastic vector field mixtures
- Broderick, T. 2014, Clusters and features from combinatorial stochastic processes
- VAswani, et al, 2014, Attention Is All You Need
- Bahdanau, et al, 2014, Neural Machine Translation by Jointly Learning to Align and Translate
- ProbLog
- D. Fierens, G. Van den Broeck, 2015. Inference and learning in probabilistic logic programs using weighted Boolean formulas.
- L. De Raedt, A. Kimmig and H. Toivonen, 2017. ProbLog: A probabilistic Prolog and its application in link discovery.
- Probabilistic Circuit
- Yitao Liang, Guy Van den Broeck, Learning Logistic Circuits
- Sutton, Barto, 2018, Reinforcement learning (2ed Edition)
- Martin L. Puterman, 2014, Markov Decision Processes: Discrete Stochastic Dynamic Programming
- Francois-Lavet, et al 2018, An Introduction to Deep Reinforcement Learning
- Bubeck, Cesa-Bianchi, 2012, Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
- Ziebart, 2010, Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy
- Levin, 2018, Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review
- Haarnoja, et al 2017, Reinforcement Learning with Deep Energy-Based Policies
- Szepesvari, 2009, Algorithms for Reinforcement Learning
- Reinforcement Learning (UCL)
- Deep Reinforcement Learning (CS285)
- Advanced Deep Learning & Reinforcement Learning
- Judea Pearl, Causality: Models, Reasoning and Inference
- Causality Tutorial Notebooks
- Literature collection: GAN-zoo
- Repos: Generative adversarial networks
- Matthed Thorpe, 2018, Introduction to Optimal Transport
- Peyre, Cuturi, 2018, Computational Optimal Transport, Codes and slides for OT