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Learning In The Real World: Constraints On Cost, Space, And Privacy, Matt J. Kusner
Learning In The Real World: Constraints On Cost, Space, And Privacy, Matt J. Kusner
McKelvey School of Engineering Theses & Dissertations
The sheer demand for machine learning in fields as varied as: healthcare, web-search ranking, factory automation, collision prediction, spam filtering, and many others, frequently outpaces the intended use-case of machine learning models. In fact, a growing number of companies hire machine learning researchers to rectify this very problem: to tailor and/or design new state-of-the-art models to the setting at hand.
However, we can generalize a large set of the machine learning problems encountered in practical settings into three categories: cost, space, and privacy. The first category (cost) considers problems that need to balance the accuracy of a machine learning model …