{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T01:21:28Z","timestamp":1742952088934,"version":"3.40.3"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030438227"},{"type":"electronic","value":"9783030438234"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-43823-4_3","type":"book-chapter","created":{"date-parts":[[2020,3,27]],"date-time":"2020-03-27T22:02:35Z","timestamp":1585346555000},"page":"28-43","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets"],"prefix":"10.1007","author":[{"given":"Claas","family":"V\u00f6lcker","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alejandro","family":"Molina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johannes","family":"Neumann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dirk","family":"Westermann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristian","family":"Kersting","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,3,28]]},"reference":[{"key":"3_CR1","unstructured":"Lam, H.T., et al.: One button machine for automating feature engineering in relational databases. arXiv preprint arXiv:1706.00327 (2017)"},{"key":"3_CR2","unstructured":"Anderson, M.R., et al.: Brainwash: a data system for feature engineering. In: CIDR (2013)"},{"key":"3_CR3","unstructured":"Zhou, Y., et al.: Parallel feature selection inspired by group testing. In: Advances in Neural Information Processing Systems, pp. 3554\u20133562 (2014)"},{"key":"3_CR4","unstructured":"Feurer, M., et al.: Efficient and robust automated machine learning. In: Advances in Neural Information Processing Systems, pp. 2962\u20132970 (2015)"},{"key":"3_CR5","unstructured":"Mendoza, H., et al.: Towards automatically-tuned neural networks. In: Workshop on Automatic Machine Learning, pp. 58\u201365 (2016)"},{"key":"3_CR6","unstructured":"Duvenaud, D.K., et al.: Structure discovery in nonparametric regression through compositional kernel search. In: Proceedings of ICML, pp. 1166\u20131174 (2013)"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Lloyd, J.R., et al.: Automatic construction and natural-language description of nonparametric regression models. In: Proceedings of AAAI, pp. 1242\u20131250 (2014)","DOI":"10.1609\/aaai.v28i1.8904"},{"key":"3_CR8","unstructured":"Kim, H., Teh, Y.W.: Scaling up the automatic statistician: scalable structure discovery using gaussian processes. In: AISTATS, pp. 575\u2013584 (2018)"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should I trust you? Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144. ACM (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"3_CR10","unstructured":"Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, pp. 4765\u20134774 (2017)"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Lapuschkin, S., et al.: Unmasking Clever Hans predictors and assessing what machines really learn. Nat. Commun. 10 (2019). Article no. 1096. https:\/\/www.nature.com\/articles\/s41467-019-08987-4","DOI":"10.1038\/s41467-019-08987-4"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Molina, A., et al.: Mixed sum-product networks: a deep architecture for hybrid domains. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11731"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Vergari, A., et al.: Automatic Bayesian density analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (2019)","DOI":"10.1609\/aaai.v33i01.33015207"},{"issue":"138","key":"3_CR14","first-page":"1","volume":"17","author":"VK Mansinghka","year":"2016","unstructured":"Mansinghka, V.K., et al.: CrossCat: a fully Bayesian, nonparametric method for analyzing heterogeneous, high-dimensional data. J. Mach. Learn. Res. 17(138), 1\u201349 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR15","unstructured":"Mansinghka, V.K., et al.: BayesDB: a probabilistic programming system for querying the probable implications of data. arXiv preprint arXiv:1512.05006 (2015)"},{"key":"3_CR16","volume-title":"Probabilistic Graphical Models: Principles and Techniques","author":"D Koller","year":"2009","unstructured":"Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Poon, H., Domingos, P.: Sum-product networks: a new deep architecture. In: UAI (2011)","DOI":"10.1109\/ICCVW.2011.6130310"},{"key":"3_CR18","first-page":"109:1","volume":"18","author":"SH Bach","year":"2017","unstructured":"Bach, S.H., et al.: Hinge-loss Markov random fields and probabilistic soft logic. JMLR 18, 109:1\u2013109:67 (2017)","journal-title":"JMLR"},{"key":"3_CR19","unstructured":"Tarlow, D., Givoni, I.E., Zemel, R.S.: HOP-MAP: efficient message passing with high order potentials. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. AISTATS 2010, Chia Laguna Resort, Sardinia, Italy, 13\u201315 May 2010, pp. 812\u2013819 (2010)"},{"key":"3_CR20","unstructured":"Choi, A., Darwiche, A.: On relaxing determinism in arithmetic circuits. In: Proceedings of ICML, pp. 825\u2013833 (2017)"},{"key":"3_CR21","unstructured":"Zhao, H., Melibari, M., Poupart, P.: On the relationship between sum-product networks and Bayesian networks. In: ICML (2015)"},{"key":"3_CR22","unstructured":"Peharz, R., et al.: On theoretical properties of sum-product networks. In: AISTATS (2015)"},{"key":"3_CR23","unstructured":"Gens, R., Domingos, P.: Discriminative learning of sum-product networks. In: NIPS (2012)"},{"key":"3_CR24","unstructured":"Trapp, M., et al.: Safe semi-supervised learning of sum-product networks. In: UAI (2017)"},{"key":"3_CR25","unstructured":"Zhao, H., Poupart, P., Gordon, G.J.: A unified approach for learning the parameters of sum-product networks. In: NIPS, pp. 433\u2013441 (2016)"},{"key":"3_CR26","unstructured":"Dennis, A., Ventura, D.: Learning the architecture of sum-product networks using clustering on variables. In: NIPS (2012)"},{"key":"3_CR27","unstructured":"Dennis, A., Ventura, D.: Greedy structure search for sum-product networks. In: IJCAI 2015, Buenos Aires, Argentina, pp. 932\u2013938. AAAI Press (2015). ISBN: 978-1-57735-738-4"},{"key":"3_CR28","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1007\/978-3-642-40991-2_39","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"R Peharz","year":"2013","unstructured":"Peharz, R., Geiger, B.C., Pernkopf, F.: Greedy part-wise learning of sum-product networks. In: Blockeel, H., Kersting, K., Nijssen, S., \u017delezn\u00fd, F. (eds.) ECML PKDD 2013. LNCS (LNAI), vol. 8189, pp. 612\u2013627. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40991-2_39"},{"key":"3_CR29","unstructured":"Gens, R., Domingos, P.: Learning the structure of sum-product networks. In: ICML, pp. 873\u2013880 (2013)"},{"key":"3_CR30","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1007\/978-3-319-23525-7_21","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"A Vergari","year":"2015","unstructured":"Vergari, A., Di Mauro, N., Esposito, F.: Simplifying, regularizing and strengthening sum-product network structure learning. In: Appice, A., Rodrigues, P.P., Santos Costa, V., Gama, J., Jorge, A., Soares, C. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9285, pp. 343\u2013358. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-23525-7_21"},{"key":"3_CR31","unstructured":"Lopez-Paz, D., Hennig, P., Sch\u00f6lkopf, B.: The randomized dependence coefficient. In: NIPS, pp. 1\u20139 (2013)"},{"key":"3_CR32","unstructured":"Kluyver, T., et al.: Jupyter notebooks - a publishing format for reproducible computational workflows. In: Loizides, F., Schmidt, B. (eds.) Positioning and Power in Academic Publishing: Players, Agents and Agendas, pp. 87\u201390. IOS Press (2016)"},{"key":"3_CR33","unstructured":"Molnar, C.: Interpretable Machine Learning (2018). https:\/\/christophm.github.io\/interpretable-ml-book\/. Accessed 4 June 2019"},{"key":"3_CR34","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1109\/TKDE.2007.190734","volume":"20","author":"M Robnik-\u0160ikonja","year":"2008","unstructured":"Robnik-\u0160ikonja, M., Kononenko, I.: Explaining classifications for individual instances. IEEE Trans. Knowl. Data Eng. 20, 589\u2013600 (2008)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3_CR35","unstructured":"Baehrens, D., et al.: How to explain individual classification decisions. J. Mach. Learn. Res. 11, 1803\u20131831 (2010)"},{"issue":"3","key":"3_CR36","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/s10115-013-0679-x","volume":"41","author":"E \u0160trumbelj","year":"2013","unstructured":"\u0160trumbelj, E., Kononenko, I.: Explaining prediction models and individual predictions with feature contributions. Knowl. Inf. Syst. 41(3), 647\u2013665 (2013). https:\/\/doi.org\/10.1007\/s10115-013-0679-x","journal-title":"Knowl. Inf. Syst."},{"issue":"10","key":"3_CR37","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","volume":"2","author":"SM Lundberg","year":"2018","unstructured":"Lundberg, S.M., et al.: Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. 2(10), 749 (2018)","journal-title":"Nat. Biomed. Eng."},{"key":"3_CR38","doi-asserted-by":"crossref","unstructured":"Molina, A., et al.: Mixed sum-product networks: a deep architecture for hybrid domains. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.11731"},{"key":"3_CR39","unstructured":"Molina, A., et al.: SPFlow: an easy and extensible library for deep probabilistic learning using sum-product networks (2019). eprint: arXiv:1901.03704"},{"key":"3_CR40","unstructured":"Dheeru, D., Taniskidou, E.K.: UCI machine learning repository. Technical report University of California, Irvine, School of Information and Computer Sciences (2017)"},{"key":"3_CR41","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"3_CR42","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1001\/jamacardio.2016.0695","volume":"1","author":"JT Neumann","year":"2016","unstructured":"Neumann, J.T., et al.: Diagnosis of myocardial infarction using a high-sensitivity troponin I 1-hour algorithm. JAMA Cardiol. 1(4), 397\u2013404 (2016)","journal-title":"JAMA Cardiol."},{"key":"3_CR43","unstructured":"V\u00f6lcker, C.: DeepNotebooks - interactive data analysis using sum- product networks. B.Sc. thesis. TU Darmstadt (2018)"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-43823-4_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T01:12:46Z","timestamp":1707786766000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-43823-4_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030438227","9783030438234"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-43823-4_3","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"28 March 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"W\u00fcrzburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2019.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"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":"733","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":"130","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":"18% - 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":"3.04","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":"5.3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ECML PKDD Workshops Information: single-blind review, submissions: 200, full papers accepted: 70, short papers accepted: 46","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}