Datamap-Driven Tabular Coreset Selection for Classifier Training

Summary: Datamap-driven algorithm that constructs user-sized tabular coresets from GBDT datamaps in minutes, producing models that match or exceed full-dataset performance. Also introduces a datamap-based inference-time enhancement with provable guarantees, plus explainability, coreset-size tuning, and robustness to frequent feature additions. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 14244
Venue: VLDB
Year: 2025
Pagerank: 4.1945683e-05
Overall Rank: 10,881 | 24.31%
DOI: 10.14778/3712221.3712249

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,455	Sentence to Model: Cost-Effective Data Collection LLM Agent	2025	SIGMOD	4.1945683e-05

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
1,204	VerdictDB: Universalizing Approximate Query Processing	2018	SIGMOD	0.00013319541
1,260	Dynamic Sample Selection for Approximate Query Processing	2003	SIGMOD	0.00012993347
1,667	Structured Search Result Differentiation	2009	VLDB	0.00010960247
2,501	DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models	2019	SIGMOD	8.6453446e-05
7,179	Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning	2023	VLDB	4.8078895e-05
7,494	SubStrat: A Subset-Based Optimization Strategy for Faster AutoML	2023	VLDB	4.7180617e-05

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