
A Generalization of the Directed Graph Layering Problem
The Directed Layering Problem (DLP) solves a step of the widely used lay...
read it

Efficient Neural Causal Discovery without Acyclicity Constraints
Learning the structure of a causal graphical model using both observatio...
read it

Learning Large DAGs by Combining Continuous Optimization and Feedback Arc Set Heuristics
Bayesian networks represent relations between variables using a directed...
read it

Masked GradientBased Causal Structure Learning
Learning causal graphical models based on directed acyclic graphs is an ...
read it

Low Rank Directed Acyclic Graphs and Causal Structure Learning
Despite several important advances in recent years, learning causal stru...
read it

DAGs with NO TEARS: Smooth Optimization for Structure Learning
Estimating the structure of directed acyclic graphs (DAGs, also known as...
read it

Graphbased Cooperative Caching in FogRAN
In this paper, the cooperative caching problem in fog radio access netwo...
read it
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 iteratively through subproblem optimization. To further improve efficiency, we propose a novel learning framework to model and learn the weighted adjacency matrices in the DAG space directly. Specifically, we first show that the set of weighted adjacency matrices of DAGs are equivalent to the set of weighted gradients of graph potential functions, and one may perform structure learning by searching in this equivalent set of DAGs. To instantiate this idea, we propose a new algorithm, DAGNoCurl, which solves the optimization problem efficiently with a twostep procedure: 1) first we find an initial cyclic solution to the optimization problem, and 2) then we employ the Hodge decomposition of graphs and learn an acyclic graph by projecting the cyclic graph to the gradient of a potential function. Experimental studies on benchmark datasets demonstrate that our method provides comparable accuracy but better efficiency than baseline DAG structure learning methods on both linear and generalized structural equation models, often by more than one order of magnitude.
READ FULL TEXT
Comments
There are no comments yet.