Leva: Boosting Machine Learning Performance with Relational Embedding Data Augmentation
Summary: Leva constructs a relational embedding by graphifying the database and learning vectors that summarize the entire data. Downstream supervision filters noisy graph signals, reducing cross-relational feature engineering and data-discovery burden, and boosting ML performance on classification/regression tasks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zixuan Zhao (University of Chicago)
- 2. Raul Castro Fernandez (University of Chicago)
BibTeX Citation
@inproceedings{zhao_sigmod22,
title = {{Leva: Boosting Machine Learning Performance with Relational Embedding Data Augmentation}},
author = {Zhao, Zixuan and Fernandez, Raul Castro},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3517891},
url = {https://dl.acm.org/doi/10.1145/3514221.3517891},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
Showing 12 of 12 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 377 | TURL: Table Understanding through Representation Learning | 2021 | VLDB | 0.00019564011 |
| 457 | Distributed Representations of Tuples for Entity Resolution | 2018 | VLDB | 0.00017899824 |
| 779 | To Join or Not to Join? Thinking Twice about Joins before Feature Selection | 2016 | SIGMOD | 0.00014048128 |
| 1,038 | ARDA: Automatic Relational Data Augmentation for Machine Learning | 2020 | VLDB | 0.000123653 |
| 1,319 | LSH Ensemble: Internet-Scale Domain Search | 2016 | VLDB | 0.00011042864 |
| 1,336 | Auctus: A Dataset Search Engine for Data Discovery and Augmentation | 2021 | VLDB | 0.00010984121 |
| 1,391 | Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks | 2020 | SIGMOD | 0.00010812249 |
| 3,092 | Correlation Sketches for Approximate Join-Correlation Queries | 2021 | SIGMOD | 7.6548729e-05 |
| 3,673 | Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers? | 2018 | VLDB | 7.1075403e-05 |
| 7,880 | Learning Over Dirty Data Without Cleaning | 2020 | SIGMOD | 5.4330096e-05 |
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