**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
- 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
Ihler, Alexander:
- 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
Minka:
- Yuan Qi, 2005, Extending Expectation Propagation for Graphical Models
- Thomas P Minka, 2001, A family of algorithms for approximate Bayesian inference
- 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
Hand-crafted message passing, BP, GBP, Tree-reweighted BP and EP, PowerEP, EC
- Wainwright and Willsky, 2003, Exact MAP estimates by hypertree agreement
- Wainwright et al, 2003, tree-reweighted belief propagation algorithms and approximated ML esimation by pseudo-moment matching
- 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
- Convergence Analysis, Roosta, 2008, Convergence Analysis of Reweighted Sum-Product Algorithms
- Opper, Winther, 2005, Expectation Consistent Approximate Inference
- Fletcher, 2017, Expectation Consistent Approximate Inference: Generalizations and Convergence
- Conditioning and Clamping
- 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
- 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
- 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
- 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
- Karaletsos, 2016, Adversarial Message Passing For Graphical Models
- Xiong et al, 2019, One-Shot Inference in Markov Random Fields
- Stoller et al, 2020, Training Generative Adversarial Networks from Incomplete Observations using Factorised Discriminators
Learning messages
- Heess et al, Learning to Pass Expectation Propagation Messages, half-automated message passing, message-level automation
- 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
- 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
- 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
- Inverse autoregreeeive flow as in previous subsection.
Learning graphical model parameters by approximate inference
- Domke, 2013, Learning Graphical Model Parameters with Approximate Marginal 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
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
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
- 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
- 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