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Learning from Censored and Truncated Data in Practice

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Stefanou-stefanou-meng-eecs-2022-thesis.pdf

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Author(s)
Stefanou, Patroklos N.
Advisor(s)
Daskalakis, Constantinos
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
An experimental study of the methods and algorithms developed to learn from truncated data. In my work, I provide a theoretical framework used to learn from missing data, and then show results from the package that I have developed to alleviate such biases.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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