Doing More with Less: Characterizing Dataset Downsampling for AutoML
Summary: Downsampling large tabular data reshapes AutoML search under fixed time budgets. Empirical study of a genetic-programming AutoML search reveals tradeoffs between pipeline quality and search efficiency, guiding scalable AutoML for big data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Fatjon Zogaj (ETH Zurich)
- 2. José Pablo Cambronero (Massachusetts Institute of Technology)
- 3. Martin C. Rinard (Massachusetts Institute of Technology)
- 4. Jürgen Cito (Massachusetts Institute of Technology; Technical University of Vienna)
BibTeX Citation
@article{zogaj_vldb21,
title = {{Doing More with Less: Characterizing Dataset Downsampling for AutoML}},
author = {Zogaj, Fatjon and Cambronero, José Pablo and Rinard, Martin C. and Cito, Jürgen},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {11},
pages = {2059--2072},
doi = {10.14778/3476249.3476262},
url = {https://doi.org/10.14778/3476249.3476262},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,089 | AutoCTS: Automated Correlated Time Series Forecasting | 2022 | VLDB | 6.2800456e-05 |
| 10,722 | CAPS: Cost-Aware ML Pipeline Selection | 2026 | VLDB | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 624 | Data Lake Management: Challenges and Opportunities | 2019 | VLDB | 0.00015479826 |
| 975 | Democratizing Data Science through Interactive Curation of ML Pipelines | 2019 | SIGMOD | 0.00012750518 |
| 1,152 | Cerebro: A Data System for Optimized Deep Learning Model Selection | 2020 | VLDB | 0.00011801961 |
| 1,193 | Weld: A Common Runtime for High Performance Data Analytics | 2017 | CIDR | 0.0001158809 |
| 1,568 | HELIX: Holistic Optimization for Accelerating Iterative Machine Learning | 2019 | VLDB | 0.0001021302 |
| 1,614 | Compressed Linear Algebra for Large-Scale Machine Learning | 2016 | VLDB | 0.00010071891 |
| 2,264 | An Intermediate Representation for Optimizing Machine Learning Pipelines | 2019 | VLDB | 8.7289107e-05 |
| 7,613 | The Machine Learning Bazaar: Harnessing the ML Ecosystem for Effective System Development | 2020 | SIGMOD | 5.4839307e-05 |
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