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Optimizing Data Acquisition to Enhance Machine Learning Performance

Summary: Introduces IAS, an online clustering-based acquisition method that incrementally updates the target model (avoiding full retraining) and uses adaptive scores to balance exploration vs. exploitation when selecting clusters. Extends to IAS-AMS which picks adaptive mini-batches from multiple clusters to remove single-cluster bias; IAS gives best efficiency while IAS-AMS yields superior labeling effectiveness with runtime comparable to CTS. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13376
Venue
VLDB
Year
2024
Pagerank
4.5435639e-05
Overall Rank
8,281 | 42.40%
DOI
10.14778/3648160.3648172

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,392 Shapley Value Estimation Based on Differential Matrix 2025 SIGMOD 4.1945683e-05
10,465 A Cost-Effective LLM-based Approach to Identify Wildlife Trafficking in Online Marketplaces 2025 SIGMOD 4.1945683e-05
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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