Complaint-driven Training Data Debugging for Query 2.0
Summary: Rain is a complaint-driven debugger for training data in Query 2.0, letting users lodge complaints on outputs to prune minimal training-set fixes. Two influence-function-based heuristics enable linear retraining, achieving high recall@k and interactive performance on real-world datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Weiyuan Wu
- 2. Lampros Flokas
- 3. Eugene Wu
- 4. Jiannan Wang
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| 7,993 | RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems | 2025 | VLDB | 4.6080455e-05 |
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| 8,853 | Complaint-Driven Training Data Debugging at Interactive Speeds | 2022 | SIGMOD | 4.4308213e-05 |