Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities
Summary: Chameleon uses foundation models for fairness-aware, multimodal data augmentation, generating few tuples to improve minority coverage. Quality/outlier checks and guidance strategies preserve semantic integrity while reducing downstream model unfairness. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Mahdi Erfanian (University of Illinois Chicago)
- 2. H. V. Jagadish (University of Michigan)
- 3. Abolfazl Asudeh (University of Illinois Chicago)
BibTeX Citation
@article{erfanian_vldb24,
title = {{Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minorities}},
author = {Erfanian, Mahdi and Jagadish, H. V. and Asudeh, Abolfazl},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {3470--3483},
doi = {10.14778/3681954.3682014},
url = {https://doi.org/10.14778/3681954.3682014},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,238 | Fair-Count-Min: Frequency Estimation under Equal Group-wise Approximation Factor | 2026 | SIGMOD | 5.093636e-05 |
| 10,305 | Weighted Set Multi-Cover on Bounded Universe and Applications in Package Recommendation | 2026 | SIGMOD | 5.093636e-05 |
| 10,511 | On Fair Epsilon Net and Geometric Hitting Set | 2026 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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