IEEE Transactions on Pattern Analysis and Machine Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Convex optimization techniques for fitting sparse Gaussian graphical models
ICML '06 Proceedings of the 23rd international conference on Machine learning
Using modified Lasso regression to learn large undirected graphs in a probabilistic framework
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A Recursive Method for Structural Learning of Directed Acyclic Graphs
The Journal of Machine Learning Research
Network-Based Inference of Cancer Progression from Microarray Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Learning structurally consistent undirected probabilistic graphical models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Structure learning on large scale common sense statistical models of human state
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Learning graphical game models
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
The Journal of Machine Learning Research
Network-based inference of cancer progression from microarray data
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
The importance of dilution in the inference of biological networks
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Effective structure learning for EDA via L1-regularizedbayesian networks
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Learning an L1-regularized Gaussian Bayesian network in the equivalence class space
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discovery of conservation laws via matrix search
DS'10 Proceedings of the 13th international conference on Discovery science
Multi-objective optimization with joint probabilistic modeling of objectives and variables
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns
ACM Transactions on Sensor Networks (TOSN)
Brain effective connectivity modeling for alzheimer's disease by sparse gaussian bayesian network
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse Linear Identifiable Multivariate Modeling
The Journal of Machine Learning Research
A partial correlation-based Bayesian network structure learning algorithm under SEM
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Inferring Networks of Diffusion and Influence
ACM Transactions on Knowledge Discovery from Data (TKDD)
The IMAP hybrid method for learning gaussian bayes nets
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Regularized continuous estimation of distribution algorithms
Applied Soft Computing
Spatial topic modeling in online social media for location recommendation
Proceedings of the 7th ACM conference on Recommender systems
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Sparsity-promoting L1-regularization has recently been succesfully used to learn the structure of undirected graphical models. In this paper, we apply this technique to learn the structure of directed graphical models. Specifically, we make three contributions. First, we show how the decomposability of the MDL score, plus the ability to quickly compute entire regularization paths, allows us to efficiently pick the optimal regularization parameter on a per-node basis. Second, we show how to use L1 variable selection to select the Markov blanket, before a DAG search stage. Finally, we show how L1 variable selection can be used inside of an order search algorithm. The effectiveness of these L1-based approaches are compared to current state of the art methods on 10 datasets.