Datamap-Driven Tabular Coreset Selection for Classifier Training
Summary: Uses Gradient-Boosted-Tree datamaps to select tabular training coresets in minutes, matching or surpassing full-data and baseline models. Datamap-guided inference enhancement offers guarantees under a stated property, plus explainability and coreset-size optimization. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Aviv Hadar (Tel Aviv University)
- 2. Tova Milo (Tel Aviv University)
- 3. Kathy Razmadze (Tel Aviv University)
BibTeX Citation
@article{hadar_vldb25,
title = {{Datamap-Driven Tabular Coreset Selection for Classifier Training}},
author = {Hadar, Aviv and Milo, Tova and Razmadze, Kathy},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {3},
pages = {876--888},
doi = {10.14778/3712221.3712249},
url = {https://doi.org/10.14778/3712221.3712249},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,736 | Sentence to Model: Cost-Effective Data Collection LLM Agent | 2025 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 772 | VerdictDB: Universalizing Approximate Query Processing | 2018 | SIGMOD | 0.00014147905 |
| 909 | Dynamic Sample Selection for Approximate Query Processing | 2003 | SIGMOD | 0.00013291205 |
| 1,799 | DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models | 2019 | SIGMOD | 9.7326398e-05 |
| 2,379 | Structured Search Result Differentiation | 2009 | VLDB | 8.666326e-05 |
| 7,112 | Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning | 2023 | VLDB | 5.6990782e-05 |
| 7,127 | SubStrat: A Subset-Based Optimization Strategy for Faster AutoML | 2023 | VLDB | 5.6957765e-05 |
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