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Description:: Modern data sets are increasingly vast, not only in the number of samples, but also in the number of measurements, or features, that they contain. This high-dimensionality poses a unique set of problems for data analysis due to a set of phenomena known as ``the curse of dimensionality.'' This thesis...
Keyword:: Data Visualization, Circular Linear Regression, Dimensionality Reduction, Curse of Dimensionality, and Quality Assessment
Subject:: Applied mathematics, Bioinformatics, and Computer science
Creator:: Johnson, Eric Mitchell
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 06/22/2022
Date Created:: 2022-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_892588 and http://dissertations.umi.com/northwestern:15988

Description:: Existing nonlinear optimization methods have proven reliable over the past few decades for a wide range of applications but have critically relied on accurate function and gradient evaluations. Modern nonlinear optimization problems arising from machine learning and scientific computing applications are increasingly complex and large scale, which make accurate evaluations...
Keyword:: Finite differences, Nonlinear optimization, Stochastic optimization, Noisy optimization, Quasi-Newton methods, and Derivative-free optimization
Subject:: Applied mathematics, Operations research, and Computer science
Creator:: Shi, Hao-Jun Michael
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/02/2022
Date Created:: 2021-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_848013 and http://dissertations.umi.com/northwestern:15785

Description:: The ever growing desire for accurate estimation and efficient learning necessitates the efforts to quantitatively characterize uncertainties for models. In this thesis, four problems pertaining to uncertainty quantification are discussed: A sequential stopping framework of constructing fixed-precision confidence regions is proposed for a class of multivariate simulation problems where variance...
Keyword:: Ranking and Selection, Reinforcement Learning, Statistical Learning, Stochastic Gradient Descent, and Uncertainty Quantification
Subject:: Applied mathematics, Statistics, and Computer science
Creator:: Zhu, Yi
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 01/21/2021
Date Created:: 2020-01-01
Resource Type:: Dissertation
Alternate Identifier:: http://dissertations.umi.com/northwestern:15080 and etdadmin_upload_741745

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