{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/122148"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/122148","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Bayesian optimization with Gaussian processes: Insights from hyperspectral trait search","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","abstract_has_math":false,"creators":["Azam, Ruhana"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Koyejo, Sanmi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12","date_published":"2023-12","updated_at":"2026-07-22T22:25:00Z","subjects":["Bayesian Optimization","Gaussian Process","Genomic Prediction"],"languages":["en","eng"],"rights":["Copyright 2023 Ruhana Azam"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/122148","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koyejo, Sanmi"]},{"key":"dc:creator","label":"Author","values":["Azam, Ruhana"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-12","2023-12-04"]},{"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":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Bayesian Optimization","Gaussian Process","Genomic Prediction"]}]},{"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 2023 Ruhana Azam"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/122148"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","The student, Ruhana Azam, accepted the attached license on 2023-11-28 at 14:42.","The student, Ruhana Azam, submitted this Thesis for approval on 2023-11-28 at 16:13.","This Thesis was approved for publication on 2023-12-04 at 11:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20045 on 2024-03-01 at 13:31:16","The application of Bayesian Optimization using Gaussian Processes (BO-GP) for global optimization problems is ubiquitous across scientific disciplines because, beyond good performance, it supports exact inference, is interpretable, and has straightforward uncertainty quantification. In this thesis, we reexamine the biological application of BO-GP in searching trait spaces for genomic prediction, which uses genome-wide marker information to predict breeding values for agronomically important traits. Genomic predictions help breeders select desirable plants earlier in the field season without waiting to observe traits later. To reduce costs of collecting data for genomic prediction models, low-cost, hyperspectral data, which are highly correlated with desired traits, can be utilized as a proxy. While these hyperspectral spaces are known to be sharp and aperiodic, BO-GP is considered a feasible approach. However, our work finds that a simple random search surprisingly achieves comparable performance to BO-GP while requiring significantly less computing cost. Through a careful investigation, we can explain this observation as a limitation of the standard implementation and use of BO-GP (e.g., the default use of Matérn kernels), for sharp and aperiodic functions -- where the incompatible structure results in samples similar to random search but with higher computational cost."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Bayesian optimization with Gaussian processes: Insights from hyperspectral trait search"]}]}],"canonical_facts":{"dc:contributor":["Koyejo, Sanmi"],"dc:creator":["Azam, Ruhana"],"dc:date":["2023-12","2023-12-04"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01","The student, Ruhana Azam, accepted the attached license on 2023-11-28 at 14:42.","The student, Ruhana Azam, submitted this Thesis for approval on 2023-11-28 at 16:13.","This Thesis was approved for publication on 2023-12-04 at 11:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20045 on 2024-03-01 at 13:31:16","The application of Bayesian Optimization using Gaussian Processes (BO-GP) for global optimization problems is ubiquitous across scientific disciplines because, beyond good performance, it supports exact inference, is interpretable, and has straightforward uncertainty quantification. In this thesis, we reexamine the biological application of BO-GP in searching trait spaces for genomic prediction, which uses genome-wide marker information to predict breeding values for agronomically important traits. Genomic predictions help breeders select desirable plants earlier in the field season without waiting to observe traits later. To reduce costs of collecting data for genomic prediction models, low-cost, hyperspectral data, which are highly correlated with desired traits, can be utilized as a proxy. While these hyperspectral spaces are known to be sharp and aperiodic, BO-GP is considered a feasible approach. However, our work finds that a simple random search surprisingly achieves comparable performance to BO-GP while requiring significantly less computing cost. Through a careful investigation, we can explain this observation as a limitation of the standard implementation and use of BO-GP (e.g., the default use of Matérn kernels), for sharp and aperiodic functions -- where the incompatible structure results in samples similar to random search but with higher computational cost."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/122148"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Ruhana Azam"],"dc:subject":["Bayesian Optimization","Gaussian Process","Genomic Prediction"],"dc:title":["Bayesian optimization with Gaussian processes: Insights from hyperspectral trait search"],"dc:type":["text"],"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":"2026-07-22T22:25:00Z"}