{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124309"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124309","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Topics on statistical inference with model uncertainty","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":["Deshmukh, Aditya Omprakash"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Veeravalli, Venugopal V","Moulin, Pierre","Raginsky, Maxim","Fellouris, Georgios"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["Statistical Inference","Model Uncertainty","Information Theory"],"languages":["en","eng"],"rights":["Copyright 2024 Aditya Deshmukh"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124309","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Veeravalli, Venugopal V","Moulin, Pierre","Raginsky, Maxim","Fellouris, Georgios"]},{"key":"dc:creator","label":"Author","values":["Deshmukh, Aditya Omprakash"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-17"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["Statistical Inference","Model Uncertainty","Information Theory"]}]},{"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 Aditya Deshmukh"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124309"]}]},{"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, Aditya Deshmukh, accepted the attached license on 2024-04-16 at 22:55.","The student, Aditya Deshmukh, submitted this Dissertation for approval on 2024-04-16 at 23:05.","This Dissertation was approved for publication on 2024-04-17 at 14:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20452 on 2024-09-16 at 00:34:46","Statistical inference is a method of data analysis used for drawing conclusions about underlying probability distributions in a statistical model and making decisions based on inferred knowledge. Classically, the theory of statistical inference was developed for the purposes of hypothesis testing and estimation of model parameters. With the advent of machine learning, several new challenging data-driven inference problems have been emerging, in which knowledge of underlying models available to the decision-maker is incomplete or ambiguous. In this dissertation, we explore and study broadly three problems in the area of statistical inference under model uncertainty. In these problems, the uncertainty arises due to the fact that either partial or no knowledge of ground truth data distributions is assumed. We approach these problems using techniques from statistics, optimization, and information theory, to understand their fundamental limits and develop theory-based algorithms with guarantees. These three problems lie in diverse sub-fields, namely, sequential controlled sensing for composite multi-hypothesis testing, robust mean estimation, and distributed feature compression. In the problems of controlled sensing and robust mean estimation, our main contributions are optimal algorithms based on statistical analysis, which are guaranteed to achieve information-theoretic limits, and exhibit competitive empirical performance. In the problem of distributed feature compression, our main contribution is a distributed compression scheme for pretrained learning models, which is based on the form of optimal quantizers derived for pretrained linear regressors assuming knowledge of underlying data distribution. In all problems discussed, we demonstrate effectiveness of proposed algorithms through experiments."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Topics on statistical inference with model uncertainty"]}]}],"canonical_facts":{"dc:contributor":["Veeravalli, Venugopal V","Moulin, Pierre","Raginsky, Maxim","Fellouris, Georgios"],"dc:creator":["Deshmukh, Aditya Omprakash"],"dc:date":["2024-05","2024-04-17"],"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, Aditya Deshmukh, accepted the attached license on 2024-04-16 at 22:55.","The student, Aditya Deshmukh, submitted this Dissertation for approval on 2024-04-16 at 23:05.","This Dissertation was approved for publication on 2024-04-17 at 14:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20452 on 2024-09-16 at 00:34:46","Statistical inference is a method of data analysis used for drawing conclusions about underlying probability distributions in a statistical model and making decisions based on inferred knowledge. Classically, the theory of statistical inference was developed for the purposes of hypothesis testing and estimation of model parameters. With the advent of machine learning, several new challenging data-driven inference problems have been emerging, in which knowledge of underlying models available to the decision-maker is incomplete or ambiguous. In this dissertation, we explore and study broadly three problems in the area of statistical inference under model uncertainty. In these problems, the uncertainty arises due to the fact that either partial or no knowledge of ground truth data distributions is assumed. We approach these problems using techniques from statistics, optimization, and information theory, to understand their fundamental limits and develop theory-based algorithms with guarantees. These three problems lie in diverse sub-fields, namely, sequential controlled sensing for composite multi-hypothesis testing, robust mean estimation, and distributed feature compression. In the problems of controlled sensing and robust mean estimation, our main contributions are optimal algorithms based on statistical analysis, which are guaranteed to achieve information-theoretic limits, and exhibit competitive empirical performance. In the problem of distributed feature compression, our main contribution is a distributed compression scheme for pretrained learning models, which is based on the form of optimal quantizers derived for pretrained linear regressors assuming knowledge of underlying data distribution. In all problems discussed, we demonstrate effectiveness of proposed algorithms through experiments."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124309"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Aditya Deshmukh"],"dc:subject":["Statistical Inference","Model Uncertainty","Information Theory"],"dc:title":["Topics on statistical inference with model uncertainty"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"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"}