{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:14:44Z","timestamp":1742991284297,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031216855"},{"type":"electronic","value":"9783031216862"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21686-2_27","type":"book-chapter","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T08:30:15Z","timestamp":1668760215000},"page":"385-397","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Time Robust Trees: Using Temporal Invariance to\u00a0Improve Generalization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7153-4323","authenticated-orcid":false,"given":"Luis","family":"Moneda","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2297-6349","authenticated-orcid":false,"given":"Denis","family":"Mau\u00e1","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,19]]},"reference":[{"key":"27_CR1","unstructured":"Arjovsky, M., Bottou, L., Gulrajani, I., Lopez-Paz, D.: Invariant risk minimization (2019)"},{"key":"27_CR2","unstructured":"Bagnell, J.A.: Robust supervised learning. In: AAAI, pp. 714\u2013719 (2005)"},{"key":"27_CR3","first-page":"280","volume":"27","author":"E Bareinboim","year":"2014","unstructured":"Bareinboim, E., Pearl, J.: Transportability from multiple environments with limited experiments: completeness results. Adv. Neural. Inf. Process. Syst. 27, 280\u2013288 (2014)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"27_CR4","unstructured":"Bishop, C.M.: Pattern recognition and machine learning. springer (2006)"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Breiman, L.: Random forest. Mach. Learn. 45(1), 5\u201332 (2001)","DOI":"10.1023\/A:1010933404324"},{"issue":"1","key":"27_CR6","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1086\/367876","volume":"70","author":"N Cartwright","year":"2003","unstructured":"Cartwright, N.: Two theorems on invariance and causality. Philos. Sci. 70(1), 203\u2013224 (2003)","journal-title":"Philos. Sci."},{"key":"27_CR7","unstructured":"City of Chicago : Chicago crime - bigquery dataset (2021), version 1. Accessed 13 Mar 2021. https:\/\/www.kaggle.com\/chicago\/chicago-crime"},{"key":"27_CR8","unstructured":"D\u2019Amour, A., et al.: Underspecification presents challenges for credibility in modern machine learning. CoRR (2020). http:\/\/arxiv.org\/abs\/2011.03395v1"},{"key":"27_CR9","unstructured":"Daoud, J.: Animal shelter dataset (2021), version 1. Accessed 13 Mar 2021. https:\/\/www.kaggle.com\/jackdaoud\/animal-shelter-analytics"},{"key":"27_CR10","unstructured":"Goyal, A., et al.: Recurrent independent mechanisms. arXiv preprint arXiv:1909.10893 (2019)"},{"key":"27_CR11","unstructured":"Gulrajani, I., Lopez-Paz, D.: In search of lost domain generalization. arXiv preprint arXiv:2007.01434 (2020)"},{"key":"27_CR12","unstructured":"Hu, W., Niu, G., Sato, I., Sugiyama, M.: Does distributionally robust supervised learning give robust classifiers? In: International Conference on Machine Learning, pp. 2029\u20132037. PMLR (2018)"},{"key":"27_CR13","unstructured":"Karimi, K., Hamilton, H.J.: Generation and interpretation of temporal decision rules. arXiv preprint arXiv:1004.3334 (2010)"},{"key":"27_CR14","unstructured":"Karimi, K., Hamilton, H.J.: Temporal rules and temporal decision trees: A C4. 5 approach. Department of Computer Science, University of Regina Regina, Saskatchewan $$\\ldots $$ (2001)"},{"key":"27_CR15","unstructured":"Ke, N.R., et al.: Learning neural causal models from unknown interventions. arXiv preprint arXiv:1910.01075 (2019)"},{"key":"27_CR16","unstructured":"Locatello, F., Poole, B., R\u00e4tsch, G., Sch\u00f6lkopf, B., Bachem, O., Tschannen, M.: Weakly-supervised disentanglement without compromises. In: International Conference on Machine Learning, pp. 6348\u20136359. PMLR (2020)"},{"key":"27_CR17","unstructured":"Mitchell, T.M., et al.: Machine learning (1997)"},{"key":"27_CR18","unstructured":"Moneda, L.: Globo esporte news dataset (2020), version 11. Accessed 31 Mar 2021. https:\/\/www.kaggle.com\/lgmoneda\/ge-soccer-clubs-news"},{"key":"27_CR19","unstructured":"Mouill\u00e9, M.: Kickstarter projects dataset (2018), version 7. Accessed 13 Mar 2021. https:\/\/www.kaggle.com\/kemical\/kickstarter-projects?select=ks-projects-201612.csv"},{"key":"27_CR20","doi-asserted-by":"publisher","unstructured":"Pearl, J.: Causality. Cambridge University Press, Cambridge, UK, 2nd edn. (2009). https:\/\/doi.org\/10.1017\/CBO9780511803161","DOI":"10.1017\/CBO9780511803161"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Pearson, K.: On a form of spurious correlation which may arise when indices are useed in the measurement of organs. In: Royal Society of London Proceedings, vol. 60, pp. 489\u2013502 (1897)","DOI":"10.1098\/rspl.1896.0076"},{"key":"27_CR22","doi-asserted-by":"crossref","unstructured":"Peters, J., B\u00fchlmann, P., Meinshausen, N.: Causal inference using invariant prediction: identification and confidence intervals. arXiv preprint arXiv:1501.01332 (2015)","DOI":"10.1111\/rssb.12167"},{"key":"27_CR23","unstructured":"Peters, J., Janzing, D., Schlkopf, B.: Elements of causal inference: foundations and learning algorithms. The MIT Press (2017)"},{"key":"27_CR24","unstructured":"Rabanser, S., G\u00fcnnemann, S., Lipton, Z.C.: Failing loudly: an empirical study of methods for detecting dataset shift (2018)"},{"key":"27_CR25","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 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"27_CR26","unstructured":"Sch\u00f6lkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., Mooij, J.: On causal and anticausal learning. arXiv preprint arXiv:1206.6471 (2012)"},{"key":"27_CR27","unstructured":"Shastry, A.: San francisco building permits dataset (2018), version 1. Accessed 13 Mar 2021. https:\/\/www.kaggle.com\/aparnashastry\/building-permit-applications-data"},{"key":"27_CR28","unstructured":"Sionek, A.: Brazilian e-commerce public dataset by olist (2019), version 7. Accessed 13 Mar 2021. https:\/\/www.kaggle.com\/olistbr\/brazilian-ecommerce"},{"issue":"2","key":"27_CR29","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1111\/j.2517-6161.1974.tb00994.x","volume":"36","author":"M Stone","year":"1974","unstructured":"Stone, M.: Cross-validatory choice and assessment of statistical predictions. J. Roy. Stat. Soc.: Ser. B (Methodol.) 36(2), 111\u2013133 (1974)","journal-title":"J. Roy. Stat. Soc.: Ser. B (Methodol.)"},{"key":"27_CR30","unstructured":"Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)"},{"issue":"523","key":"27_CR31","doi-asserted-by":"publisher","first-page":"1228","DOI":"10.1080\/01621459.2017.1319839","volume":"113","author":"S Wager","year":"2018","unstructured":"Wager, S., Athey, S.: Estimation and inference of heterogeneous treatment effects using random forests. J. Am. Stat. Assoc. 113(523), 1228\u20131242 (2018)","journal-title":"J. Am. Stat. Assoc."},{"key":"27_CR32","unstructured":"Wilson, A.C., Roelofs, R., Stern, M., Srebro, N., Recht, B.: The marginal value of adaptive gradient methods in machine learning. arXiv preprint arXiv:1705.08292 (2017)"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21686-2_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T01:16:08Z","timestamp":1728436568000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21686-2_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031216855","9783031216862"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21686-2_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Campinas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www2.sbc.org.br\/bracis2022\/","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":"JEMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"225","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":"89","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":"40% - 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","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":"4","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)"}}]}}