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Inductive transfer for Bayesian network structure learning. In Artificial intelligence and statistics. PMLR 339--346.  Alexandru Niculescu-Mizil and Rich Caruana. 2007. Inductive transfer for Bayesian network structure learning. In Artificial intelligence and statistics. PMLR 339--346."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-015-9570-9"},{"key":"e_1_3_2_2_34_1","volume-title":"Twenty-Sixth AAAI conference on artificial intelligence.","author":"Oyen Diane","year":"2012","unstructured":"Diane Oyen and Terran Lane . 2012 . Leveraging domain knowledge in multitask Bayesian network structure learning . In Twenty-Sixth AAAI conference on artificial intelligence. Diane Oyen and Terran Lane. 2012. Leveraging domain knowledge in multitask Bayesian network structure learning. In Twenty-Sixth AAAI conference on artificial intelligence."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2013.90"},{"key":"e_1_3_2_2_36_1","volume-title":"An overview of general performance metrics of binary classifier systems. arXiv preprint arXiv:1410.5330","author":"Raschka Sebastian","year":"2014","unstructured":"Sebastian Raschka . 2014. An overview of general performance metrics of binary classifier systems. arXiv preprint arXiv:1410.5330 ( 2014 ). Sebastian Raschka. 2014. An overview of general performance metrics of binary classifier systems. arXiv preprint arXiv:1410.5330 (2014)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.01.005"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1080\/24725854.2022.2039813"},{"key":"e_1_3_2_2_39_1","volume-title":"prediction, and search","author":"Spirtes Peter","unstructured":"Peter Spirtes , Clark N Glymour , Richard Scheines , and David Heckerman . 2000. Causation , prediction, and search . MIT press . Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman. 2000. Causation, prediction, and search. 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PMLR, 11898--11908."},{"key":"e_1_3_2_2_47_1","volume-title":"International Conference on Machine Learning. PMLR, 7154--7163","author":"Yu Yue","year":"2019","unstructured":"Yue Yu , Jie Chen , Tian Gao , and Mo Yu . 2019 . DAG-GNN: DAG structure learning with graph neural networks . In International Conference on Machine Learning. PMLR, 7154--7163 . Yue Yu, Jie Chen, Tian Gao, and Mo Yu. 2019. DAG-GNN: DAG structure learning with graph neural networks. In International Conference on Machine Learning. PMLR, 7154--7163."},{"key":"e_1_3_2_2_48_1","volume-title":"CTM-based Propagation of Non-recurrent Congestion and Location of Variable Message Sign. In 2012 Fifth International Joint Conference on Computational Sciences and Optimization. IEEE, 462--465","author":"Zhang Aomuhan","year":"2012","unstructured":"Aomuhan Zhang and Ziyou Gao . 2012 . CTM-based Propagation of Non-recurrent Congestion and Location of Variable Message Sign. In 2012 Fifth International Joint Conference on Computational Sciences and Optimization. IEEE, 462--465 . Aomuhan Zhang and Ziyou Gao. 2012. CTM-based Propagation of Non-recurrent Congestion and Location of Variable Message Sign. In 2012 Fifth International Joint Conference on Computational Sciences and Optimization. IEEE, 462--465."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3070203"},{"key":"e_1_3_2_2_50_1","volume-title":"Dags with no tears: Continuous optimization for structure learning. Advances in Neural Information Processing Systems 31","author":"Zheng Xun","year":"2018","unstructured":"Xun Zheng , Bryon Aragam , Pradeep K Ravikumar , and Eric P Xing . 2018. Dags with no tears: Continuous optimization for structure learning. Advances in Neural Information Processing Systems 31 ( 2018 ). Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing. 2018. Dags with no tears: Continuous optimization for structure learning. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_2_2_51_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 3414--3425","author":"Zheng Xun","year":"2020","unstructured":"Xun Zheng , Chen Dan , Bryon Aragam , Pradeep Ravikumar , and Eric Xing . 2020 . Learning sparse nonparametric dags . In International Conference on Artificial Intelligence and Statistics. PMLR, 3414--3425 . Xun Zheng, Chen Dan, Bryon Aragam, Pradeep Ravikumar, and Eric Xing. 2020. Learning sparse nonparametric dags. In International Conference on Artificial Intelligence and Statistics. PMLR, 3414--3425."},{"key":"e_1_3_2_2_52_1","unstructured":"Yun Zhou Jiang Wang Cheng Zhu and Weiming Zhang. 2017. Multiple dags learning with non-negative matrix factorization. In Advanced Methodologies for Bayesian Networks. PMLR 81--92.  Yun Zhou Jiang Wang Cheng Zhu and Weiming Zhang. 2017. Multiple dags learning with non-negative matrix factorization. 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