2 papers
cs.LG2024
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
Shuyu Dong, Michèle Sebag, Kento Uemura +4
Causal learning tackles the computationally demanding task of estimating causal graphs. This paper introduces a new divide-and-conquer approach for causal graph learning, called DC…
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…