Data Acquisition for Improving Model Confidence

Summary: Targets data acquisition for *model confidence* rather than accuracy: select limited samples from a large pool to maximize confidence gains. Proposes bulk/sequential acquisition, kNN-based approximations, and a distribution-based variant for broad applicability; validated across datasets/models. (summarized by gpt-5.4-mini on May 24 2026)

Paper ID: 6896
Venue: SIGMOD
Year: 2024
Pagerank: 4.1945683e-05
Overall Rank: 10,955 | 23.79%
DOI: 10.1145/3654934

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1,463	ARDA: Automatic Relational Data Augmentation for Machine Learning	2020	VLDB	0.00011869295
1,891	Towards Model-based Pricing for Machine Learning in a Data Marketplace	2019	SIGMOD	0.00010194092
2,359	Data Market Platforms: Trading Data Assets to Solve Data Problems	2020	VLDB	8.9607667e-05
3,142	Active Learning for ML Enhanced Database Systems	2020	SIGMOD	7.4815444e-05
3,750	Data Acquisition for Improving Machine Learning Models	2021	VLDB	6.7895763e-05
4,129	Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers?	2018	VLDB	6.428887e-05
4,138	Protecting Data Markets from Strategic Buyers	2022	SIGMOD	6.4175758e-05
4,863	Data-Sharing Markets: Model, Protocol, and Algorithms to Incentivize the Formation of Data-Sharing Consortia	2023	SIGMOD	5.8697471e-05
5,024	Towards Distribution-aware Query Answering in Data Markets	2022	VLDB	5.7535043e-05
5,381	Selective Data Acquisition in the Wild for Model Charging	2022	VLDB	5.5399508e-05
5,976	Responsible Data Integration: Next-generation Challenges	2022	SIGMOD	5.245976e-05

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