{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/125761"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/125761","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Uncertainty quantification in machine learning with Bayesian models","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01","abstract_has_math":false,"creators":["Qian, Christopher"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":["Liang, Feng","Li, Bo","Simpson, Douglas","Adams, Jason"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-07-02","date_published":"2024-07-02","updated_at":"2026-07-22T22:25:02Z","subjects":["Recalibration","Epistemic Uncertainty","Dropout"],"languages":["en","eng"],"rights":["Copyright 2024 Christopher Qian"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/125761","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Liang, Feng","Li, Bo","Simpson, Douglas","Adams, Jason"]},{"key":"dc:creator","label":"Author","values":["Qian, Christopher"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-07-02","2024-08"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Recalibration","Epistemic Uncertainty","Dropout"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Christopher Qian"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/125761"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01","The student, Christopher Qian, accepted the attached license on 2024-06-25 at 21:25.","The student, Christopher Qian, submitted this Dissertation for approval on 2024-06-25 at 21:36.","This Dissertation was approved for publication on 2024-07-02 at 16:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20875 on 2025-02-04 at 21:25:05","Uncertainty quantification plays a vital role to the adoption of modern machine learning methods in real-world applications by improving the trustworthiness and reliability of complex models. In the following chapters, we develop methods in uncertainty quantification that address several major areas of current research. The first two chapters focus on developing novel recalibration methods that can be applied to pre-trained models to improve their probabilistic predictions. In the classification setting, we extend the standard temperature scaling method by identifying one of its main characteristics of always increasing the uncertainty of the prediction and developing a method that enforces this property. In the regression setting, we introduce an optimization framework for optimization for which we can recover the well-known quantile recalibration method, and we use this framework to propose a novel method. In the third chapter, we propose a novel epistemic uncertainty quantification method and show that it faithfully targets the formal definition of epistemic uncertainty in terms of accuracy gain. All of our methods are designed with Bayesian methods in mind; the methods of Chapters 3 and 4 are specifically used with Bayesian models, and the method of Chapter 2 can be extended to Bayesian models."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Uncertainty quantification in machine learning with Bayesian models"]}]}],"canonical_facts":{"dc:contributor":["Liang, Feng","Li, Bo","Simpson, Douglas","Adams, Jason"],"dc:creator":["Qian, Christopher"],"dc:date":["2024-07-02","2024-08"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-08-01","The student, Christopher Qian, accepted the attached license on 2024-06-25 at 21:25.","The student, Christopher Qian, submitted this Dissertation for approval on 2024-06-25 at 21:36.","This Dissertation was approved for publication on 2024-07-02 at 16:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20875 on 2025-02-04 at 21:25:05","Uncertainty quantification plays a vital role to the adoption of modern machine learning methods in real-world applications by improving the trustworthiness and reliability of complex models. In the following chapters, we develop methods in uncertainty quantification that address several major areas of current research. The first two chapters focus on developing novel recalibration methods that can be applied to pre-trained models to improve their probabilistic predictions. In the classification setting, we extend the standard temperature scaling method by identifying one of its main characteristics of always increasing the uncertainty of the prediction and developing a method that enforces this property. In the regression setting, we introduce an optimization framework for optimization for which we can recover the well-known quantile recalibration method, and we use this framework to propose a novel method. In the third chapter, we propose a novel epistemic uncertainty quantification method and show that it faithfully targets the formal definition of epistemic uncertainty in terms of accuracy gain. All of our methods are designed with Bayesian methods in mind; the methods of Chapters 3 and 4 are specifically used with Bayesian models, and the method of Chapter 2 can be extended to Bayesian models."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/125761"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Christopher Qian"],"dc:subject":["Recalibration","Epistemic Uncertainty","Dropout"],"dc:title":["Uncertainty quantification in machine learning with Bayesian models"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Statistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:02Z"}