{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T13:56:40Z","timestamp":1726235800848},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466601"},{"type":"electronic","value":"9783031466618"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-46661-8_29","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:29Z","timestamp":1699102949000},"page":"431-446","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CD-BNN: Causal Discovery with\u00a0Bayesian Neural Network"],"prefix":"10.1007","author":[{"given":"Huaxu","family":"Han","sequence":"first","affiliation":[]},{"given":"Shuliang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Hanning","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Sijie","family":"Ruan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"29_CR1","unstructured":"Blundell, C., Cornebise, J., Kavukcuoglu, K., Wierstra, D.: Weight uncertainty in neural network. In: International Conference on Machine Learning, pp. 1613\u20131622. PMLR (2015)"},{"key":"29_CR2","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1613\/jair.2681","volume":"35","author":"R Daly","year":"2009","unstructured":"Daly, R., Shen, Q.: Learning Bayesian network equivalence classes with ant colony optimization. J. Artif. Intell. Res. 35, 391\u2013447 (2009)","journal-title":"J. Artif. Intell. Res."},{"key":"29_CR3","unstructured":"Geffner, T., et al.: Deep end-to-end causal inference. In: NeurIPS 2022 Workshop on Causality for Real-world Impact (2022). https:\/\/openreview.net\/forum?id=6DPVXzjnbDK"},{"key":"29_CR4","unstructured":"Gelman, A.: Bayesian model-building by pure thought: some principles and examples. Statistica Sinica 6(1), 215\u2013232 (1996)"},{"key":"29_CR5","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.ins.2016.01.090","volume":"348","author":"S Gheisari","year":"2016","unstructured":"Gheisari, S., Meybodi, M.R.: BNC-PSO: structure learning of Bayesian networks by particle swarm optimization. Inf. Sci. 348, 272\u2013289 (2016)","journal-title":"Inf. Sci."},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"He, Y., Cui, P., Shen, Z., Xu, R., Liu, F., Jiang, Y.: Daring: differentiable causal discovery with residual independence. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 596\u2013605 (2021)","DOI":"10.1145\/3447548.3467439"},{"issue":"3","key":"29_CR7","doi-asserted-by":"publisher","first-page":"118","DOI":"10.3923\/itj.2006.540.545","volume":"5","author":"XC Heng","year":"2006","unstructured":"Heng, X.C., Qin, Z., Wang, X.H., Shao, L.P.: Research on learning Bayesian networks by particle swarm optimization. Inf. Technol. J. 5(3), 118\u2013121 (2006)","journal-title":"Inf. Technol. J."},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Kuang, K., Cui, P., Athey, S., Xiong, R., Li, B.: Stable prediction across unknown environments. In: proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1617\u20131626 (2018)","DOI":"10.1145\/3219819.3220082"},{"key":"29_CR9","unstructured":"Lachapelle, S., Brouillard, P., Deleu, T., Lacoste-Julien, S.: Gradient-based neural dag learning. arXiv preprint arXiv:1906.02226 (2019)"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Li, X.L., Wang, S.C., He, X.D.: Learning Bayesian networks structures based on memory binary particle swarm optimization. In: International Conference on Simulated Evolution and Learning (2006)","DOI":"10.1007\/11903697_72"},{"issue":"1","key":"29_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1752-0509-1-37","volume":"1","author":"R Opgen-Rhein","year":"2007","unstructured":"Opgen-Rhein, R., Strimmer, K.: From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst. Biol. 1(1), 1\u201310 (2007)","journal-title":"BMC Syst. Biol."},{"key":"29_CR12","unstructured":"Peters, J., Mooij, J.M., Janzing, D., Sch\u00f6lkopf, B.: Causal discovery with continuous additive noise models (2014)"},{"issue":"5721","key":"29_CR13","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1126\/science.1105809","volume":"308","author":"K Sachs","year":"2005","unstructured":"Sachs, K., Perez, O., Pe\u2019er, D., Lauffenburger, D.A., Nolan, G.P.: Causal protein-signaling networks derived from multiparameter single-cell data. Science 308(5721), 523\u2013529 (2005)","journal-title":"Science"},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Shanmugam, R.: Causality: models, reasoning, and inference-judea pearl; Cambridge University Press, Cambridge, UK, 2000, pp. 384. ISBN 0-521-77362-8. Neurocomputing 1(41), 189\u2013190 (2001)","DOI":"10.1016\/S0925-2312(01)00330-7"},{"key":"29_CR15","unstructured":"Shimizu, S., Hoyer, P.O., Hyv\u00e4rinen, A., Kerminen, A., Jordan, M.: A linear non-gaussian acyclic model for causal discovery. J. Mach. Learn. Res. 7(10) (2006)"},{"key":"29_CR16","unstructured":"Spirtes, P., Glymour, C., Scheines, R.: Causality from probability (1989)"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Spirtes, P., Glymour, C.N., Scheines, R., Heckerman, D.: Causation, prediction, and search. MIT press (2000)","DOI":"10.7551\/mitpress\/1754.001.0001"},{"issue":"2","key":"29_CR18","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s10115-009-0239-6","volume":"24","author":"W Tong","year":"2010","unstructured":"Tong, W., Yang, J.: A heuristic method for learning Bayesian networks using discrete particle swarm optimization. Knowl. Inform. Syst. 24(2), 269\u2013281 (2010)","journal-title":"Knowl. Inform. Syst."},{"issue":"27","key":"29_CR19","doi-asserted-by":"publisher","first-page":"7310","DOI":"10.1073\/pnas.1510479113","volume":"113","author":"HR Varian","year":"2016","unstructured":"Varian, H.R.: Causal inference in economics and marketing. Proc. Natl. Acad. Sci. 113(27), 7310\u20137315 (2016)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"6","key":"29_CR20","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1073\/pnas.6.6.320","volume":"6","author":"S Wright","year":"1920","unstructured":"Wright, S.: The relative importance of heredity and environment in determining the piebald pattern of guinea-pigs. Proc. Natl. Acad. Sci. U.S.A. 6(6), 320 (1920)","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"29_CR21","unstructured":"Yu, Y., Chen, J., Gao, T., Yu, M.: DAG-GNN: dag structure learning with graph neural networks. In: International Conference on Machine Learning, pp. 7154\u20137163. PMLR (2019)"},{"issue":"3","key":"29_CR22","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1016\/j.cell.2013.03.030","volume":"153","author":"B Zhang","year":"2013","unstructured":"Zhang, B., et al.: Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer\u2019s disease. Cell 153(3), 707\u2013720 (2013)","journal-title":"Cell"},{"key":"29_CR23","unstructured":"Zhang, K., Hyvarinen, A.: On the identifiability of the post-nonlinear causal model. arXiv preprint arXiv:1205.2599 (2012)"},{"key":"29_CR24","unstructured":"Zhang, M., Jiang, S., Cui, Z., Garnett, R., Chen, Y.: D-VAE: a variational autoencoder for directed acyclic graphs. In: Advances in Neural Information Processing Systems 32 (2019)"},{"key":"29_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, X., Jia, S., Li, X., Cong, G.: Learning the bayesian networks structure based on ant colony optimization and differential evolution. In: 2018 4th International Conference on Control, Automation and Robotics (ICCAR) (2018)","DOI":"10.1109\/ICCAR.2018.8384700"},{"key":"29_CR26","unstructured":"Zheng, X., Aragam, B., Ravikumar, P.K., Xing, E.P.: Dags with no tears: Continuous optimization for structure learning. In: Advances in Neural Information Processing Systems 31 (2018)"},{"key":"29_CR27","unstructured":"Zheng, X., Dan, C., Aragam, B., Ravikumar, P., Xing, E.: Learning sparse nonparametric DAGs. In: International Conference on Artificial Intelligence and Statistics, pp. 3414\u20133425. PMLR (2020)"},{"key":"29_CR28","unstructured":"Zhu, S., Ng, I., Chen, Z.: Causal discovery with reinforcement learning. arXiv preprint arXiv:1906.04477 (2019)"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46661-8_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:05:59Z","timestamp":1699103159000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46661-8_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466601","9783031466618"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46661-8_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"216","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.97","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.77","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}