[Colloquium] Alex Zhao MS Presentation/May 21, 2021

[Jessica Garza]

This is an announcement of Alex Zhao's MS Presentation. Alex is a student in the Bx/MS program.

Zoom details will follow.

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Date: Friday, May 21, 2021

Time: 8:30 AM, CST

Location: remote via Zoom

M.S. Candidate: Alex Zhao

M.S. Paper Title: Boosting Machine Learning Performance with Relational Embedding Data Augmentation

Advisor: Raul Castro Fernandez

Committee Members: Aaron Elmore, Sanjay Krishnan, and Raul Castro Fernandez


Abstract:
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Training machine learning models on relational data involves identifying schema relations, combining tables, and engineering features. Extensive human effort in the process poses difficulty for users who are unfamiliar with the dataset and creates an awkward dilemma where users have to forgo performance boost to save effort. In our research, we present Leva, an end-to-end system that produces distributed vector representations of relational data that can be directly fed as inputs for downstream machine learning tasks. Leva processes and embeds information across different tables into compact vector representations without any prior knowledge of database schema. When used as featurized inputs for downstream tasks, the embedding (1) saves the trouble of relation identification, join execution and feature engineering (2) closely resembles, and in some cases, outperforms, heavily-engineered joining solutions. We show that using embedding as featurized inputs generalizes well across tasks and datasets.
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Jessica Garza
Assistant Director of Undergraduate Studies
Department of Computer Science
The University of Chicago
Covid-19 Resources <https://cs.uchicago.edu/remote2020/>
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