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On Efficient Approximate Queries over Machine Learning Models

Summary: Framework for approximate queries over ML predictions that minimizes expensive oracle (human/DNN) calls by combining cheap proxy scores with selective oracle sampling for precision- and recall-target queries. Two regimes—Proxy Quality (PQA/PQE) and Core Set Closure (CSC/CSE)—offer provable guarantees and empirically outperform prior work. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13343
Venue
VLDB
Year
2023
Pagerank
4.3524472e-05
Overall Rank
9,351 | 34.95%
DOI
10.14778/3574245.3574273

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