Related Work
+ DAG Structure Learning
+ Additive Noise Models
+ Identifiability of DAGs
+ AMP Chain Graph Models
- Submodularity Optimization (theory)
- Submodularity + Greedy Search (implementation)-
2013-Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints
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2017- Submodular Functions and Machine Learning
- Submodular Functions [Part 1] [Part 2]
- Submodular Functions and Machine Learning [Lecture 1] [Lecture 2] [Lecture 3]
- Literature survey
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Lecture: Submodularity and Optimization -- Jeff Bilmes (Part 1) (Part 2) (Part 3)
- 2002-Optimal Structure Identification With Greedy Search
- 2011-Identifiability of Causal Graphs using Functional Models [Link]
- 2013-Identifiability of Gaussian structural equation models with equal error variances [Link]
- 2015-Concave Penalized Estimation of Sparse Gaussian Bayesian Networks [Link]
- 2018-DAGs with NO TEARS- Continuous Optimization for Structure Learning [Link]
- 2019-On Causal Discovery with Equal Variance Assumption [Link]
- 2019-DAG-GNN- DAG Structure Learning with Graph Neural Networks [Link]
- 2020-On the Role of Sparsity and DAG Constraints for Learning Linear DAGs [Link]
- 2020-A polynomial-time algorithm for learning nonparametric causal graphs.pdf [Link]
- 2020-Identifiability of Additive Noise Models Using Conditional Variances [Link]
- 2020-A Bregman Method for Structure Learning on Sparse Directed Acyclic Graphs [Link]
- 2020-On the Role of Sparsity and DAG Constraints for Learning Linear DAGs [Link]
- 2020-Learning Sparse Nonparametric DAGs [Link]
- 2020-On the Convergence of Continuous Constrained Optimizaton for Structure Learning [Link]
- 2020-DAGs with No Fears- A Closer Look at Continuous Optimization for Learning Bayesian Networks [Link]
- 2006-High-dimensional Graphs and variables selection with the Lasso [Link]
- 2006-First-Order Methods for Sparse Covariance Selection [Link]
- 2007-Sparse inverse covariance estimation with the graphical Lasso [Link]
- 2011-A constrained L1 Minimization Approach to Sparse Precision Matrix Estimation [Link]
- 2012-Projected Subgradient Methods for Learning Sparse Gaussians [Link]
- 2012-The graphical lasso-New insights and alternatves [Link]
- 2001-Separation and Completeness Properties for AMP Chain Graph Markov Model [Link]
- 2005-A unified approach to the characterizationof equivalence classes of dags, chain graphs with no flagsand chain graphs. [Link]
- 2009-Discrete Chain Graph Models [Link]
- 2009-Two operations of merging and splitting components in a chain graph [Link]
- 2012-Learning AMP Chain Graphs under Faithfulness [Link]
- 2014-Marginal AMP Chain Graphs [Link]
- 2016-Alternative Markov and Causal Properties for Acyclic Directed Mixed Graphs [Link]
- 2018-Reasoning with Alternative Acyclic Directed Mixed Graphs [Link]
- 2020-AMP Chain Graphs- Minimal Separators and Structure Learning Algorithms [Link]
- 2020-Markov Blanket Discovery in AMP Chain Graphs [Link]
Bayesian Hypergraph / Chain graph structure
TODO
+ Reading material list;
+ Generate synthetic data with ground truth;
+ Introduce baseline algorithms for chain graoh structure learning;
- Likelihood Function for chain graph structure learning.[Paper]
- 1992-On block recursive linear Regression Equations [Link]
- 2001-An Alternative Markov Property for Chain Graphs [Link]
- 2002-Chain graph models and their causal interpretations [Link]
[Thesis]
- 2004-Maximum Likelihood Estimation in Gaussian AMP Chain Graph Models and Gaussian Ancestral Graph Models [Link]
- 2014-Thesis-A Study of Chain Graph Interpretations [Link]
- 2016-Thesis-Chain Graphs [Link]
[Others]
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2005-Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property [Link]
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2008-Structural Learning of Chain Graphs via Decomposition [Link]
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2014-An inclusion optimal algorithm for chain graph structure learning [Link]
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2018-Sparse Learning in Gaussian Chain Graphs for State Space Models [Link]
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2020-Learning LWF Chain Graphs: A Markov Blanket Discovery Approach [Code]
- 2006-High-dimensional Graphs and variables selection with the Lasso [Link]
- 2011-A constrained L1 Minimization Approach to Sparse Precision Matrix Estimation [Link]
- 2012-Discovering Cyclic Causal Models by Independent Components Analysis [Link]
- 2017-Gaussian Graphical Models [Link]
- 2020-Learning Sparse Nonparametric DAGs [Link] [Code]