Search Results for author:
Found 4 papers, 4 papers with code
Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
In the context of linear structural equation models (SEMs), this paper focuses on learning causal structures from the inverse covariance matrix.
From graphs to DAGs: a low-complexity model and a scalable algorithm
Learning directed acyclic graphs (DAGs) is long known a critical challenge at the core of probabilistic and causal modeling.
New Riemannian preconditioned algorithms for tensor completion via polyadic decomposition
We propose new Riemannian preconditioned algorithms for low-rank tensor completion via the polyadic decomposition of a tensor.
Alternating minimization algorithms for graph regularized tensor completion
The usage of graph regularization entails benefits in the learning accuracy of LRTC, but at the same time, induces coupling graph Laplacian terms that hinder the optimization of the tensor completion model.
