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Learning to Validate the Predictions of Black Box Classifiers on Unseen Data

Summary: Learns a performance predictor for pretrained black-box classifiers using programmatic specifications of dataset shift and data errors, without distributional assumptions. Alarms on predicted accuracy drops on unseen serving data and outperforms baselines across datasets and error types. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5817
Venue
SIGMOD
Year
2020
Pagerank
6.4428544e-05
Overall Rank
4,110 | 71.41%
DOI
10.1145/3318464.3380604

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

Rank Cited Paper Year Venue Pagerank
2,158 Uni-Detect: A Unified Approach to Automated Error Detection in Tables 2019 SIGMOD 9.4141354e-05
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