{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:10:42Z","timestamp":1776888642147,"version":"3.51.2"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030865221","type":"print"},{"value":"9783030865238","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-86523-8_1","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T06:05:16Z","timestamp":1631253916000},"page":"3-18","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep Conditional Transformation Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8066-1615","authenticated-orcid":false,"given":"Philipp F. M.","family":"Baumann","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8301-0471","authenticated-orcid":false,"given":"Torsten","family":"Hothorn","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8772-9202","authenticated-orcid":false,"given":"David","family":"R\u00fcgamer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,11]]},"reference":[{"issue":"2","key":"1_CR1","doi-asserted-by":"publisher","first-page":"1148","DOI":"10.1214\/18-AOS1709","volume":"47","author":"S Athey","year":"2019","unstructured":"Athey, S., Tibshirani, J., Wager, S., et al.: Generalized random forests. Ann. Stat. 47(2), 1148\u20131178 (2019)","journal-title":"Ann. Stat."},{"issue":"2","key":"1_CR2","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1111\/j.2517-6161.1964.tb00553.x","volume":"26","author":"GE Box","year":"1964","unstructured":"Box, G.E., Cox, D.R.: An analysis of transformations. J. Roy. Stat. Soc. Ser. B (Methodol.) 26(2), 211\u2013243 (1964)","journal-title":"J. Roy. Stat. Soc. Ser. B (Methodol.)"},{"issue":"6","key":"1_CR3","doi-asserted-by":"publisher","first-page":"2205","DOI":"10.3982\/ECTA10582","volume":"81","author":"V Chernozhukov","year":"2013","unstructured":"Chernozhukov, V., Fern\u00e1ndez-Val, I., Melly, B.: Inference on counterfactual distributions. Econometrica 81(6), 2205\u20132268 (2013)","journal-title":"Econometrica"},{"key":"1_CR4","unstructured":"Depeweg, S., Hernandez-Lobato, J.M., Doshi-Velez, F., Udluft, S.: Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning. In: International Conference on Machine Learning, pp. 1184\u20131193. PMLR (2018)"},{"key":"1_CR5","unstructured":"Durkan, C., Bekasov, A., Murray, I., Papamakarios, G.: Cubic-spline flows. arXiv preprint arXiv:1906.02145 (2019)"},{"key":"1_CR6","unstructured":"Durkan, C., Bekasov, A., Murray, I., Papamakarios, G.: Neural spline flows. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019 Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019)"},{"key":"1_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-34333-9","volume-title":"Regression: Models, Methods and Applications","author":"L Fahrmeir","year":"2013","unstructured":"Fahrmeir, L., Kneib, T., Lang, S., Marx, B.: Regression: Models, Methods and Applications. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-34333-9"},{"issue":"6","key":"1_CR8","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.cagd.2012.03.001","volume":"29","author":"RT Farouki","year":"2012","unstructured":"Farouki, R.T.: The Bernstein polynomial basis: a centennial retrospective. Comput. Aided Geom. Des. 29(6), 379\u2013419 (2012)","journal-title":"Comput. Aided Geom. Des."},{"issue":"430","key":"1_CR9","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1080\/01621459.1995.10476537","volume":"90","author":"S Foresi","year":"1995","unstructured":"Foresi, S., Peracchi, F.: The conditional distribution of excess returns: an empirical analysis. J. Am. Stat. Assoc. 90(430), 451\u2013466 (1995)","journal-title":"J. Am. Stat. Assoc."},{"issue":"3","key":"1_CR10","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1111\/j.2517-6161.1994.tb01996.x","volume":"56","author":"AE Gelfand","year":"1994","unstructured":"Gelfand, A.E., Dey, D.K.: Bayesian model choice: asymptotics and exact calculations. J. Roy. Stat. Soc. Ser. B (Methodol.) 56(3), 501\u2013514 (1994)","journal-title":"J. Roy. Stat. Soc. Ser. B (Methodol.)"},{"issue":"109","key":"1_CR11","first-page":"1","volume":"17","author":"M Gupta","year":"2016","unstructured":"Gupta, M., et al.: Monotonic calibrated interpolated look-up tables. J. Mach. Learn. Res. 17(109), 1\u201347 (2016)","journal-title":"J. Mach. Learn. Res."},{"issue":"2\u20133","key":"1_CR12","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/S0951-8320(96)00077-4","volume":"54","author":"SC Hora","year":"1996","unstructured":"Hora, S.C.: Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management. Reliab. Eng. Syst. Saf. 54(2\u20133), 217\u2013223 (1996)","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"1","key":"1_CR13","first-page":"1","volume":"92","author":"T Hothorn","year":"2020","unstructured":"Hothorn, T.: Most likely transformations: the mlt package. J. Stat. Softw. Articles 92(1), 1\u201368 (2020)","journal-title":"J. Stat. Softw. Articles"},{"issue":"1","key":"1_CR14","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s11222-019-09870-4","volume":"30","author":"T Hothorn","year":"2019","unstructured":"Hothorn, T.: Transformation boosting machines. Stat. Comput. 30(1), 141\u2013152 (2019). https:\/\/doi.org\/10.1007\/s11222-019-09870-4","journal-title":"Stat. Comput."},{"key":"1_CR15","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1111\/rssb.12017","volume":"76","author":"T Hothorn","year":"2014","unstructured":"Hothorn, T., Kneib, T., B\u00fchlmann, P.: Conditional transformation models. J. R. Stat. Soc. Ser. B Stat. Methodol. 76, 3\u201327 (2014)","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"issue":"1","key":"1_CR16","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1111\/sjos.12291","volume":"45","author":"T Hothorn","year":"2018","unstructured":"Hothorn, T., M\u00f6st, L., B\u00fchlmann, P.: Most likely transformations. Scand. J. Stat. 45(1), 110\u2013134 (2018)","journal-title":"Scand. J. Stat."},{"key":"1_CR17","doi-asserted-by":"publisher","unstructured":"Hothorn, T., Zeileis, A.: Predictive distribution modeling using transformation forests. J. Comput. Graph. Stat. 1\u201316 (2021). https:\/\/doi.org\/10.1080\/10618600.2021.1872581","DOI":"10.1080\/10618600.2021.1872581"},{"key":"1_CR18","unstructured":"H\u00fcllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: a tutorial introduction. arXiv preprint arXiv:1910.09457 (2019)"},{"key":"1_CR19","unstructured":"Jaini, P., Selby, K.A., Yu, Y.: Sum-of-squares polynomial flow. CoRR (2019)"},{"key":"1_CR20","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems, pp. 5574\u20135584 (2017)"},{"key":"1_CR21","unstructured":"Klein, N., Hothorn, T., Kneib, T.: Multivariate conditional transformation models. arXiv preprint arXiv:1906.03151 (2019)"},{"key":"1_CR22","doi-asserted-by":"publisher","unstructured":"Kobyzev, I., Prince, S., Brubaker, M.: Normalizing flows: an introduction and review of current methods. IEEE Trans. Pattern Anal. Mach. Intell. 1 (2020). https:\/\/doi.org\/10.1109\/tpami.2020.2992934","DOI":"10.1109\/tpami.2020.2992934"},{"key":"1_CR23","series-title":"Economic Society Monographs","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511754098","volume-title":"Quantile Regression","author":"R Koenker","year":"2005","unstructured":"Koenker, R.: Quantile Regression. Economic Society Monographs, Cambridge University Press, Cambridge (2005)"},{"key":"1_CR24","unstructured":"Kook, L., Herzog, L., Hothorn, T., D\u00fcrr, O., Sick, B.: Ordinal neural network transformation models: deep and interpretable regression models for ordinal outcomes. arXiv preprint arXiv:2010.08376 (2020)"},{"issue":"429","key":"1_CR25","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1080\/01621459.1995.10476491","volume":"90","author":"C Kooperberg","year":"1995","unstructured":"Kooperberg, C., Stone, C.J., Truong, Y.K.: Hazard regression. J. Am. Stat. Assoc. 90(429), 78\u201394 (1995)","journal-title":"J. Am. Stat. Assoc."},{"key":"1_CR26","unstructured":"Kuleshov, V., Fenner, N., Ermon, S.: Accurate uncertainties for deep learning using calibrated regression. In: Proceedings of the 35th International Conference on Machine Learning, vol. 80, pp. 2796\u20132804 (2018)"},{"key":"1_CR27","unstructured":"Leorato, S., Peracchi, F.: Comparing distribution and quantile regression. EIEF Working Papers Series 1511, Einaudi Institute for Economics and Finance (EIEF) (2015)"},{"key":"1_CR28","first-page":"983","volume":"7","author":"N Meinshausen","year":"2006","unstructured":"Meinshausen, N.: Quantile regression forests. J. Mach. Learn. Res. 7, 983\u2013999 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"1_CR29","doi-asserted-by":"crossref","unstructured":"M\u00fcller, T., McWilliams, B., Rousselle, F., Gross, M., Nov\u00e1k, J.: Neural importance sampling (2019)","DOI":"10.1145\/3341156"},{"key":"1_CR30","unstructured":"Papamakarios, G., Nalisnick, E., Rezende, D.J., Mohamed, S., Lakshminarayanan, B.: Normalizing flows for probabilistic modeling and inference (2019)"},{"key":"1_CR31","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1080\/10618600.2019.1677243","volume":"29","author":"M Pratola","year":"2019","unstructured":"Pratola, M., Chipman, H., George, E.I., McCulloch, R.: Heteroscedastic BART via multiplicative regression trees. J. Comput. Graph. Stat. 29, 405\u2013417 (2019)","journal-title":"J. Comput. Graph. Stat."},{"key":"1_CR32","unstructured":"Ramasinghe, S., Fernando, K., Khan, S., Barnes, N.: Robust normalizing flows using Bernstein-type polynomials (2021)"},{"key":"1_CR33","unstructured":"Rezende, D., Mohamed, S.: Variational inference with normalizing flows. In: Proceedings of Machine Learning Research, vol. 37, pp. 1530\u20131538 (2015)"},{"issue":"3","key":"1_CR34","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1111\/j.1467-9876.2005.00510.x","volume":"54","author":"RA Rigby","year":"2005","unstructured":"Rigby, R.A., Stasinopoulos, D.M.: Generalized additive models for location, scale and shape. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 54(3), 507\u2013554 (2005)","journal-title":"J. Roy. Stat. Soc. Ser. C (Appl. Stat.)"},{"issue":"501","key":"1_CR35","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1080\/01621459.2012.736903","volume":"108","author":"C Rothe","year":"2013","unstructured":"Rothe, C., Wied, D.: Misspecification testing in a class of conditional distributional models. J. Am. Stat. Assoc. 108(501), 314\u2013324 (2013)","journal-title":"J. Am. Stat. Assoc."},{"key":"1_CR36","unstructured":"Rothfuss, J., et al.: Noise regularization for conditional density estimation (2020)"},{"key":"1_CR37","unstructured":"R\u00fcgamer, D., Kolb, C., Klein, N.: Semi-Structured Deep Distributional Regression: Combining Structured Additive Models and Deep Learning. arXiv preprint arXiv:2002.05777 (2020)"},{"key":"1_CR38","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.ins.2013.07.030","volume":"255","author":"R Senge","year":"2014","unstructured":"Senge, R., et al.: Reliable classification: learning classifiers that distinguish aleatoric and epistemic uncertainty. Inf. Sci. 255, 16\u201329 (2014)","journal-title":"Inf. Sci."},{"key":"1_CR39","doi-asserted-by":"publisher","unstructured":"Sick, B., Hathorn, T., D\u00fcrr, O.: Deep transformation models: Tackling complex regression problems with neural network based transformation models. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 2476\u20132481 (2021). https:\/\/doi.org\/10.1109\/ICPR48806.2021.9413177","DOI":"10.1109\/ICPR48806.2021.9413177"},{"key":"1_CR40","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015)"},{"issue":"2","key":"1_CR41","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1002\/cpa.21423","volume":"66","author":"EG Tabak","year":"2013","unstructured":"Tabak, E.G., Turner, C.V.: A family of nonparametric density estimation algorithms. Commun. Pure Appl. Math. 66(2), 145\u2013164 (2013)","journal-title":"Commun. Pure Appl. Math."},{"issue":"1","key":"1_CR42","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1111\/1467-9868.00374","volume":"65","author":"SN Wood","year":"2003","unstructured":"Wood, S.N.: Thin plate regression splines. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 65(1), 95\u2013114 (2003)","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"issue":"503","key":"1_CR43","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1080\/01621459.2013.808949","volume":"108","author":"CO Wu","year":"2013","unstructured":"Wu, C.O., Tian, X.: Nonparametric estimation of conditional distributions and rank-tracking probabilities with time-varying transformation models in longitudinal studies. J. Am. Stat. Assoc. 108(503), 971\u2013982 (2013)","journal-title":"J. Am. Stat. Assoc."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86523-8_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T22:03:10Z","timestamp":1757455390000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86523-8_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030865221","9783030865238"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86523-8_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"11 September 2021","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":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"869","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":"210","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":"24% - 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":"3-4","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":"3-9","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":"The conference was held online due to the COVID-19 pandemic.","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)"}}]}}