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
Incoming Citations (Sorted by Pagerank)
Showing 15 of 15 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 35 of 35 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,696 | The Data Interaction Game | 2018 | SIGMOD | 4.3023337e-05 |
| 9,351 | On Efficient Approximate Queries over Machine Learning Models | 2023 | VLDB | 4.3524472e-05 |
| 3,472 | LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency | 2025 | VLDB | 7.0639229e-05 |
| 5,473 | Facilitating SQL Query Composition and Analysis | 2020 | SIGMOD | 5.4885366e-05 |
| 5,371 | LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning | 2022 | SIGMOD | 5.5428776e-05 |
| 3,658 | Towards a Hands-Free Query Optimizer through Deep Learning | 2019 | CIDR | 6.8704209e-05 |
| 7,989 | RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems | 2025 | VLDB | 4.6124681e-05 |
| 608 | DeepDB: Learn from Data, not from Queries! | 2020 | VLDB | 0.00019235898 |
| 5,222 | Enabling SQL-based Training Data Debugging for Federated Learning | 2022 | VLDB | 5.6210545e-05 |
| 8,853 | Complaint-Driven Training Data Debugging at Interactive Speeds | 2022 | SIGMOD | 4.4350727e-05 |