mlidea: Interactively Improving ML Data Preparation Code via "Shadow Pipelines"
Summary: mlidea generates "shadow pipelines": hidden variants of a user's ML data-prep pipeline that auto-detect issues, try modifications, and produce interactive, explainable suggestions (mislabeled rows, QC robustness, slice-level fixes). Uniquely uses incremental view maintenance to keep shadow pipelines low-latency, enabling fast interactive iteration and seamless code suggestions during development. (summarized by gpt-5-mini on Feb 09 2026)
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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 |
|---|---|---|---|---|
| 522 | Differential dataflow | 2013 | CIDR | 0.00021099241 |
| 791 | ActiveClean: Interactive Data Cleaning For Statistical Modeling | 2016 | VLDB | 0.00016629664 |
| 1,298 | Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms | 2019 | VLDB | 0.00012758104 |
| 1,404 | Responsible Data Management | 2020 | VLDB | 0.00012174977 |
| 1,867 | Interpretable Data-Based Explanations for Fairness Debugging | 2022 | SIGMOD | 0.00010272055 |
| 1,940 | SliceLine: Fast, Linear-Algebra-based Slice Finding for ML Model Debugging | 2021 | SIGMOD | 0.00010020173 |
| 5,209 | Explaining Outputs in Modern Data Analytics | 2016 | VLDB | 5.629362e-05 |
| 8,257 | Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines | 2023 | SIGMOD | 4.5487511e-05 |
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