| dc.contributor.advisor | Daskalakis, Constantinos | |
| dc.contributor.author | Stefanou, Patroklos N. | |
| dc.date.accessioned | 2022-08-29T15:55:07Z | |
| dc.date.available | 2022-08-29T15:55:07Z | |
| dc.date.issued | 2022-05 | |
| dc.date.submitted | 2022-05-27T16:18:27.475Z | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/144548 | |
| dc.description.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. | |
| dc.publisher | Massachusetts Institute of Technology | |
| dc.rights | In Copyright - Educational Use Permitted | |
| dc.rights | Copyright MIT | |
| dc.rights.uri | http://rightsstatements.org/page/InC-EDU/1.0/ | |
| dc.title | Learning from Censored and Truncated Data in Practice | |
| dc.type | Thesis | |
| dc.description.degree | M.Eng. | |
| dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
| mit.thesis.degree | Master | |
| thesis.degree.name | Master of Engineering in Electrical Engineering and Computer Science | |
