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It highlights themes including decision theory, imprecise probabilities, and the need to understand uncertainty in data-centric learning systems. The document includes an extensive acknowledgments section expressing gratitude to advisors, reviewers, collaborators, working groups, research institutions, administrative staff, and students for their support, mentorship, and feedback throughout the doctoral journey.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/uncertainty-quantification-in-data-centric-machine-learning-some-statistical-perspectives-dissertation/290681/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/uncertainty-quantification-in-data-centric-machine-learning-some-statistical-perspectives-dissertation/290681.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is the dissertation about?","Question",{"text":113,"@type":114},"The dissertation is titled \"Uncertainty Quantification in Data-Centric Machine Learning: Some Statistical Perspectives\" and centers on statistical perspectives for uncertainty quantification in data-centric machine learning contexts.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which themes are explicitly mentioned in the acknowledgments?",{"text":118,"@type":114},"The acknowledgments reference decision theory and imprecise probabilities, emphasizing the role these ideas played in the author’s intellectual development.",{"name":120,"@type":111,"acceptedAnswer":121},"Who contributed to the dissertation process according to the document?",{"text":122,"@type":114},"The document thanks supervisors, reviewers, co-authors, members of the working group, colleagues at the Department of Statistics, and multiple institutional programs and hosts for supporting the doctoral work.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290681,1789642350,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Julian Martin Rodemann  \nUncertainty Quantification  \nin Data-Centric Machine Learning: Some Statistical Perspectives  \nDissertation an der Fakultt fr Mathematik, Informatik und Statistik der Ludwig-Maximilians-Universit t M nchen  \nzur Erlangung des akademischen Doktorgrads der Naturwissenschaften (Dr. rer . nat. ) Eingereicht am 31.10.2025  \nJulian Martin Rodemann  \nUncertainty Quantification  \nin Data-Centric Machine Learning: Some Statistical Perspectives  \nDissertation an der Fakult t fr Mathematik, Informatik und Statistik der Ludwig-Maximilians-Universit t M nchen  \nVorgelegt von Julian Martin Rodemann aus T bingen am 31 .10.2025  \nBetreuer: Prof. Dr. Thomas Augustin  \nErster Berichterstatter: Prof. Dr. Thomas Nagler Zweiter Berichterstatter: Prof. Sbastien Destercke, PhD. Dritter Berichterstatter: Prof. Lev V. Utkin, PhD. , DSc.  \nTag der Einreichung: 31.10.2025  \nTag der Disputation: 06.03.2026  \nTo Lotti  \nAcknowledgments  \nI would like to express my deepest gratitude to everyone who supported me on the journey of my doctoral studies. Among the many people who helped me to complete my dissertation, an especially heartfelt thank you goes to . . .  \n. . . my supervisor Thomas Augustin for supporting me throughout the years, always having my back, sparking my interest in decision theory and imprecise probabilities and—most importantly—for trusting and believing in me even when I did not. I would like to thank you for always taking the time for my (many...) questions, for sharing fun (statistical) anecdotes and for inspiring me to always try to get to the root of a problem. You fundamentally shaped me as a statistician, but also as a person. Thank you, Thomas, for your patient, thoughtful, and generous supervision that made this work possible!  \n. . . Thomas Nagler, S´ebastien Destercke and Lev Utkin for their willingness to serve as reviewers of my PhD thesis.  \n. . . Volker Schmid for chairing the examination committee as well as Fabian Scheipl for serving as alternate.  \n. . . my co-authors Thomas Augustin, Christoph Jansen, Esteban Garc´es Arias, Hannah Blocher, Georg Schollmeyer, Matthias Aßenmacher, Christian Heumann, Meimingwei Li, Eyke H¨ullermeier, Malte Nalenz, James Bailie, Dominik Kreiß, Federico Croppi, Philipp Arens, Yusuf Sale, Julia Herbinger, Bernd Bischl, Conor J. Walsh, Giuseppe Casalicchio, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Chongsheng Zhang, Stefan Dietrich, Christoph Luther, Yuanhao Ding, Danlu Chen, Gaojuan Fan, Fabian Bongratz, Vladimir Golkov, Lukas Mautner, Luca Della Libera, Frederik Heetmeyer, Felix Czaja and Daniel  \nCremers . Thank you for your valuable hints, your continuous support and all the constructive feedback. Above all, however, I am deeply grateful for all the stimulating and long discussions we had. Research is a collaborative effort that lives from the people who pursue it. Without you, I would neither have published any papers nor completed this dissertation—and I would have had only half as much fun.  \n. . . all former and current members of the working group: Thomas Augustin, Hannah Blocher, Cornelia F¨utterer, Polina Gordienko, Lea H¨ohler, Christoph Jansen, Gilbert Kiprotich, Do minik Kreiß, Malte Nalenz, Daniel Schlichting, Georg Schollmeyer and Patrick Schwaferts. Thank you for welcoming me so warmly in the beginning, for your valuable advice and countless enjoyable lunch breaks! I would like to especially thank the (former) postdoctoral researchers Christoph Jansen and Georg Schollmeyer for supporting and guiding me at the beginning of my PhD. I have benefited a lot from your mentorship and enjoy our fruitful ongoing collaborations!  \n. . . the graduate center of the Bavarian Institute for Digital Transformation (bidt) within the Bavarian Academy of Sciences (BAS) for supporting my doctoral studies through a scholarship. The graduate center was a great place to learn about the many facets of the digital world that are beyond my own research. Moreover, I a","cbCaieUSGHGUguev","https://ap.wps.com/l/cbCaieUSGHGUguev","pdf",1986220,141,"English","# Acknowledgments\n## Gratitude to supervisors and reviewers\n## Support from collaborators and research groups\n## Institutional and administrative support\n## Proofreading and academic community contributions","[{\"question\":\"What is the dissertation about?\",\"answer\":\"The dissertation is titled \\\"Uncertainty Quantification in Data-Centric Machine Learning: Some Statistical Perspectives\\\" and centers on statistical perspectives for uncertainty quantification in data-centric machine learning contexts.\"},{\"question\":\"Which themes are explicitly mentioned in the acknowledgments?\",\"answer\":\"The acknowledgments reference decision theory and imprecise probabilities, emphasizing the role these ideas played in the author’s intellectual development.\"},{\"question\":\"Who contributed to the dissertation process according to the document?\",\"answer\":\"The document thanks supervisors, reviewers, co-authors, members of the working group, colleagues at the Department of Statistics, and multiple institutional programs and hosts for supporting the doctoral work.\"}]","Uncertainty Quantification in Data-Centric Machine Learning - Some Statistical Perspectives - Dissertation | PDF",355]