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On Detecting Cherry-picked Generalizations

Summary: Framework for detecting and explaining cherry-picked generalizations by refining aggregate queries. Scoring metric for generalization quality, efficient score computation, and explanation tasks to reveal counterexamples and alternatives. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12933
Venue
VLDB
Year
2022
Pagerank
4.1945683e-05
Overall Rank
11,419 | 20.56%
DOI
10.14778/3485450.3485457

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Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

Rank Citing Paper Year Venue Pagerank
7,172 Summarized Causal Explanations For Aggregate Views 2024 SIGMOD 4.8114797e-05
9,644 Fair and Actionable Causal Prescription Ruleset 2025 SIGMOD 4.3109001e-05
10,140 Analyzing Deviations from Monotonic Trends through Database Repair 2026 SIGMOD 4.1945683e-05
10,147 Causal Explanations for Disparate Trends: Where and Why? 2026 SIGMOD 4.1945683e-05
10,213 Stress-Testing Causal Claims via Cardinality Repairs 2026 SIGMOD 4.1945683e-05
10,740 Finding Convincing Views to Endorse a Claim 2025 VLDB 4.1945683e-05
10,809 ClaimIt: Finding Convincing Views to Endorse a Claim 2025 VLDB 4.1945683e-05
11,393 OREO: Detection of Cherry-picked Generalizations 2022 VLDB 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

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

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