Contributions to Optimal Experimental Designs with Application to the Development of Machine Learning Based Systems
2023-03
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Contributions to Optimal Experimental Designs with Application to the Development of Machine Learning Based Systems
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2023-03
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Abstract
Design of experiments (DoE) is long-established as an indispensable methodology for reliableand expeditious product development across many domains. Over the years, the application
context of DoE has evolved from agriculture experiments to industrial experiments, and
more recently, to experiments on software products. The three studies in the thesis make
methodological contributions to the optimal experimental design literature and propose
experimentation strategies for the following objectives (i) for optimizing an unknown noisy
black-box function, (ii) for estimating an unknown noisy black-box function, and (iii) for
identifying the best surrogate model among m ≥ 2 candidate models that best approximate the
unknown black-box function. The application context of the first two studies is hyperparameter
optimization of machine learning models, a critical step in their training process. The
application context of the third study is online evaluation of machine learning models, a
critical step for validating the performance of the models prior to their deployment. We
illustrate the utility of the proposed experimentation strategies through simulation studies
on synthetic test functions and two case studies at a large medical device manufacturer in
the context of automating visual quality inspections in manufacturing. Minimizing the total
cost of experimentation and shortening the experimentation lifecycle for the development of
reliable machine learning based systems are the key contributions of the methods proposed
in this thesis.
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University of Minnesota Ph.D. dissertation. March 2023. Major: Business Administration. Advisor: Christopher Nachtsheim. 1 computer file (PDF); x, 124 pages.
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Sunder, Gautham. (2023). Contributions to Optimal Experimental Designs with Application to the Development of Machine Learning Based Systems. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/257054.
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