{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115425"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115425","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Probabilistic performance metric elicitation","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2022-11-11 without embargo terms","The student, Zachary Robertson, accepted the attached license on 2022-04-21 at 13:58.","The student, Zachary Robertson, submitted this Thesis for approval on 2022-04-21 at 14:03.","This Thesis was approved for publication on 2022-04-25 at 14:50.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17885 on 2022-11-11 at 13:42:49","Metric elicitation is a type of inverse decision problem where the goal is to learn a loss function for classification using expert comparisons between candidate classifiers. However, for many practical tasks, such an expert can be noisy. Here we present a unified approach for learning metrics robust to constant and location-dependent noise models. Our approach takes advantage of the problem's similarity to probabilistic bisection search and uses pairwise comparisons to update a pseudo-belief distribution for the performance metric. Our theoretical results guarantee convergence in practical settings and extend beyond previous results to include multi-expert elicitation. Quantitative comparisons against existing methods for performance metric elicitation and inverse decision theory demonstrate the advantage of our approach."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Probabilistic performance metric elicitation"]}]}],"canonical_facts":{"dc:contributor":["Koyejo, Oluwasanmi"],"dc:creator":["Robertson, Zachary"],"dc:date":["2022-05","2022-04-25"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms","The student, Zachary Robertson, accepted the attached license on 2022-04-21 at 13:58.","The student, Zachary Robertson, submitted this Thesis for approval on 2022-04-21 at 14:03.","This Thesis was approved for publication on 2022-04-25 at 14:50.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17885 on 2022-11-11 at 13:42:49","Metric elicitation is a type of inverse decision problem where the goal is to learn a loss function for classification using expert comparisons between candidate classifiers. However, for many practical tasks, such an expert can be noisy. Here we present a unified approach for learning metrics robust to constant and location-dependent noise models. Our approach takes advantage of the problem's similarity to probabilistic bisection search and uses pairwise comparisons to update a pseudo-belief distribution for the performance metric. Our theoretical results guarantee convergence in practical settings and extend beyond previous results to include multi-expert elicitation. Quantitative comparisons against existing methods for performance metric elicitation and inverse decision theory demonstrate the advantage of our approach."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115425"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Zachary Robertson"],"dc:subject":["Machine Learning","Active Learning","Metric Selection"],"dc:title":["Probabilistic performance metric elicitation"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:54Z"}