Learning from Censored and Truncated Data in Practice
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.
Main Author: | Stefanou, Patroklos N. |
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Other Authors: | Daskalakis, Constantinos |
Format: | Thesis |
Published: |
Massachusetts Institute of Technology
2022
|
Online Access: | https://hdl.handle.net/1721.1/144548 |
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