1. Using Hierarchical Bayesian Optimization to Learn and Exploit the Dependency Structures of Combinatorial Many-Objective Decision Problems Open Access Author: Shah, Ruchit Aswin Title: Using Hierarchical Bayesian Optimization to Learn and Exploit the Dependency Structures of Combinatorial Many-Objective Decision Problems Graduate Program: Industrial Engineering Keywords: Combinatorial optimizationMultiobjective optimizationKnapsack problemProbabilistic model building evolutionary algorithHierarchical Bayesian networksVisual Analytics File: Download Thesis_Ruchit_Shah.pdf Committee Members: John Patrick Reed, Thesis Advisor/Co-AdvisorPatrick M Reed, Thesis Advisor/Co-AdvisorTimothy William Simpson, Thesis Advisor/Co-Advisor
2. Learning about and over Networks: Optimized Designs for Network Tomography and Decentralized Learning Open Access Author: Chiu, Cho Chun Title: Learning about and over Networks: Optimized Designs for Network Tomography and Decentralized Learning Graduate Program: Computer Science and Engineering Keywords: Decentralized learningD-PSGDconvergence analysisLaplacian matrix samplingcommunication costConvergence analysisActive learningWBANNetwork tomographyCombinatorial optimizationCoreset File: Download Daniel_PhD_Dissertation.pdf Committee Members: Chitaranjan Das, Program Head/ChairVijaykrishnan Narayanan, Major Field MemberFenglong Ma, Outside Unit & Field MemberTing He, Chair & Dissertation AdvisorMehrdad Mahdavi, Major Field Member
