Dags with no tears: Continuous optimization for structure learning
Estimating the structure of directed acyclic graphs (DAGs, also known as Bayesian
networks) is a challenging problem since the search space of DAGs is combinatorial and …
networks) is a challenging problem since the search space of DAGs is combinatorial and …
DAG-GNN: DAG structure learning with graph neural networks
Learning a faithful directed acyclic graph (DAG) from samples of a joint distribution is a
challenging combinatorial problem, owing to the intractable search space superexponential …
challenging combinatorial problem, owing to the intractable search space superexponential …
DAGs with no curl: An efficient DAG structure learning approach
Recently directed acyclic graph (DAG) structure learning is formulated as a constrained
continuous optimization problem with continuous acyclicity constraints and was solved …
continuous optimization problem with continuous acyclicity constraints and was solved …
Learning large-scale Bayesian networks with the sparsebn package
Learning graphical models from data is an important problem with wide applications,
ranging from genomics to the social sciences. Nowadays datasets often have upwards of …
ranging from genomics to the social sciences. Nowadays datasets often have upwards of …
Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families
Greedy algorithms have long been a workhorse for learning graphical models, and more
broadly for learning statistical models with sparse structure. In the context of learning …
broadly for learning statistical models with sparse structure. In the context of learning …
Bayesian network structure learning with integer programming: Polytopes, facets and complexity
The challenging task of learning structures of probabilistic graphical models is an important
problem within modern AI research. Recent years have witnessed several major algorithmic …
problem within modern AI research. Recent years have witnessed several major algorithmic …
Simultaneous missing value imputation and structure learning with groups
Learning structures between groups of variables from data with missing values is an
important task in the real world, yet difficult to solve. One typical scenario is discovering the …
important task in the real world, yet difficult to solve. One typical scenario is discovering the …
Integer programming for learning directed acyclic graphs from continuous data
Learning directed acyclic graphs (DAGs) from data is a challenging task both in theory and
in practice, because the number of possible DAGs scales superexponentially with the …
in practice, because the number of possible DAGs scales superexponentially with the …
Student model construction of intelligent teaching system based on Bayesian network
L Wu - Personal and Ubiquitous Computing, 2020 - Springer
The intelligent teaching system is the most important in the field of teaching. It uses artificial
intelligence technology to bring a lot of help to learners in terms of knowledge and skill …
intelligence technology to bring a lot of help to learners in terms of knowledge and skill …
Optimal transport for structure learning under missing data
Causal discovery in the presence of missing data introduces a chicken-and-egg dilemma.
While the goal is to recover the true causal structure, robust imputation requires considering …
While the goal is to recover the true causal structure, robust imputation requires considering …