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cs.LG2022
Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
Shuyu Dong, Kento Uemura, Akito Fujii +4
Learning causal structures from observational data is a fundamental problem facing important computational challenges when the number of variables is large. In the context of linea…
cs.LG2022
From graphs to DAGs: a low-complexity model and a scalable algorithm
Shuyu Dong, Michèle Sebag
Learning directed acyclic graphs (DAGs) is long known a critical challenge at the core of probabilistic and causal modeling. The NoTears approach of (Zheng et al., 2018), through a…
math.OC2022
On the analysis of optimization with fixed-rank matrices: a quotient geometric view
Shuyu Dong, Bin Gao, Wen Huang +1
We study a type of Riemannian gradient descent (RGD) algorithm, designed through Riemannian preconditioning, for optimization on -- the set of $m\times…