{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124259"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124259","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Enhancing trustworthiness in probabilistic programming: systematic approaches for robust and accurate inference","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_has_math":false,"creators":["Huang, Zixin"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Misailovic, Sasa","Marinov, Darko","Mitra, Sayan","Kang, Eunsuk"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["Probabilistic Programming","Quantized Inference","Robustness","Abstract Interpretation","Program Analysis","Machine Learning"],"languages":["en","eng"],"rights":["Copyright 2024 Zixin Huang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124259","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Misailovic, Sasa","Marinov, Darko","Mitra, Sayan","Kang, Eunsuk"]},{"key":"dc:creator","label":"Author","values":["Huang, Zixin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-15"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"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":["Probabilistic Programming","Quantized Inference","Robustness","Abstract Interpretation","Program Analysis","Machine Learning"]}]},{"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 Zixin Huang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124259"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Zixin Huang, accepted the attached license on 2024-04-12 at 00:24.","The student, Zixin Huang, submitted this Dissertation for approval on 2024-04-12 at 01:09.","This Dissertation was approved for publication on 2024-04-15 at 15:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20367 on 2024-09-16 at 00:34:00","Probabilistic programming simplifies the encoding of statistical models as straightforward programs. At its core, it employs an inference algorithm which automate the model inference, allowing developers to focus on model creation. Its simplicity has led to its growing application in critical areas such as autonomous driving, privacy modeling, computer networks, and pandemic prediction. However, the flexibility of probabilistic modeling and the scalability to large datasets come at a price of trustworthiness: the approximate inference algorithms used by many existing probabilistic programming systems may produce inaccurate results; also the collected data often contain noise, which can violate the model’s assumption and cause large deviations in the results. Trustworthiness, therefore, has two key properties: accuracy, to ensure results close to the true underlying distribution, and robustness, to ensure reliable results amidst data noise. This dissertation introduces a systematic approach, composed of multiple probabilistic programming systems throughout the probabilistic programming computation stack, to analyze and enhance the trustworthiness of probabilistic programs. We present the results of two-pronged investigation: first, we identify several trustworthiness challenges of the current practice of probabilistic programming algorithms. The examination is supported by the presentation of ASTRA, an experimental testbed for evaluating the robustness of probabilistic programs against data noise, and SixthSense, a system aiding developers in debugging convergence issues in sampling-based approximate inference algorithms. The second part of the dissertation introduces AQUA, a novel quantized inference algorithm which can achieve better accuracy than existing approximate inference algorithms and scales better than exact inference. Then, the dissertation advances the domain of probabilistic reasoning by moving beyond the conventional focus on computing a single posterior distribution. It presents AURA, an abstract interpretation which provides precise, soundly guaranteed bounds on posterior distributions when they are subjected an infinite set of data perturbations."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Enhancing trustworthiness in probabilistic programming: systematic approaches for robust and accurate inference"]}]}],"canonical_facts":{"dc:contributor":["Misailovic, Sasa","Marinov, Darko","Mitra, Sayan","Kang, Eunsuk"],"dc:creator":["Huang, Zixin"],"dc:date":["2024-05","2024-04-15"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Zixin Huang, accepted the attached license on 2024-04-12 at 00:24.","The student, Zixin Huang, submitted this Dissertation for approval on 2024-04-12 at 01:09.","This Dissertation was approved for publication on 2024-04-15 at 15:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20367 on 2024-09-16 at 00:34:00","Probabilistic programming simplifies the encoding of statistical models as straightforward programs. At its core, it employs an inference algorithm which automate the model inference, allowing developers to focus on model creation. Its simplicity has led to its growing application in critical areas such as autonomous driving, privacy modeling, computer networks, and pandemic prediction. However, the flexibility of probabilistic modeling and the scalability to large datasets come at a price of trustworthiness: the approximate inference algorithms used by many existing probabilistic programming systems may produce inaccurate results; also the collected data often contain noise, which can violate the model’s assumption and cause large deviations in the results. Trustworthiness, therefore, has two key properties: accuracy, to ensure results close to the true underlying distribution, and robustness, to ensure reliable results amidst data noise. This dissertation introduces a systematic approach, composed of multiple probabilistic programming systems throughout the probabilistic programming computation stack, to analyze and enhance the trustworthiness of probabilistic programs. We present the results of two-pronged investigation: first, we identify several trustworthiness challenges of the current practice of probabilistic programming algorithms. The examination is supported by the presentation of ASTRA, an experimental testbed for evaluating the robustness of probabilistic programs against data noise, and SixthSense, a system aiding developers in debugging convergence issues in sampling-based approximate inference algorithms. The second part of the dissertation introduces AQUA, a novel quantized inference algorithm which can achieve better accuracy than existing approximate inference algorithms and scales better than exact inference. Then, the dissertation advances the domain of probabilistic reasoning by moving beyond the conventional focus on computing a single posterior distribution. It presents AURA, an abstract interpretation which provides precise, soundly guaranteed bounds on posterior distributions when they are subjected an infinite set of data perturbations."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124259"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Zixin Huang"],"dc:subject":["Probabilistic Programming","Quantized Inference","Robustness","Abstract Interpretation","Program Analysis","Machine Learning"],"dc:title":["Enhancing trustworthiness in probabilistic programming: systematic approaches for robust and accurate inference"],"dc:type":["text"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}