ARDA: Automatic Relational Data Augmentation for Machine Learning
Summary: ARDA automates relational data augmentation for ML via joins of input data with external data to yield features. Two components: data-search-join to assemble augmented features; efficient feature selection to prune noisy features; validated on datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Nadiia Chepurko (Massachusetts Institute of Technology)
- 2. Ryan Marcus (Massachusetts Institute of Technology)
- 3. Emanuel Zgraggen (Massachusetts Institute of Technology)
- 4. Raul Castro Fernandez (University of Chicago)
- 5. Tim Kraska (Massachusetts Institute of Technology)
- 6. David Karger (Massachusetts Institute of Technology)
BibTeX Citation
@article{chepurko_vldb20,
title = {{ARDA: Automatic Relational Data Augmentation for Machine Learning}},
author = {Chepurko, Nadiia and Marcus, Ryan and Zgraggen, Emanuel and Fernandez, Raul Castro and Kraska, Tim and Karger, David},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {9},
pages = {1373--1387},
doi = {10.14778/3397230.3397235},
url = {https://doi.org/10.14778/3397230.3397235},
year = {2020}
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