no code implementations • 20 Dec 2023 • Naiyu Yin, Tian Gao, Yue Yu, Qiang Ji
We then propose an effective two-phase iterative DAG learning algorithm to address the increasing optimization difficulties and to learn a causal DAG from data with heteroscedastic variable noise under varying variance.
no code implementations • 16 Jun 2022 • Zijun Cui, Naiyu Yin, Yuru Wang, Qiang Ji
Causal discovery is to learn cause-effect relationships among variables given observational data and is important for many applications.
1 code implementation • 14 Jun 2021 • Yue Yu, Tian Gao, Naiyu Yin, Qiang Ji
To further improve efficiency, we propose a novel learning framework to model and learn the weighted adjacency matrices in the DAG space directly.