MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines
Summary: MLINSPECT: a data distribution debugger for ML pipelines, using lightweight lineage-based inspection to pinpoint distribution bugs in preprocessing. It operates on declarative estimator/transformer abstractions, handles relational and matrix data, and requires no manual code instrumentation. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,869 | Interpretable Data-Based Explanations for Fairness Debugging | 2022 | SIGMOD | 0.00010263235 |
| 6,644 | Query Refinement for Diversity Constraint Satisfaction | 2024 | VLDB | 4.973836e-05 |
| 8,119 | mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses Over and Over? | 2023 | VLDB | 4.5779426e-05 |
| 8,183 | SHiFT: An Efficient, Flexible Search Engine for Transfer Learning | 2023 | VLDB | 4.5615358e-05 |
| 8,253 | Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines | 2023 | SIGMOD | 4.5444167e-05 |
| 9,116 | Towards Observability for Production Machine Learning Pipelines | 2022 | VLDB | 4.3886184e-05 |
| 10,289 | Efficient Query Repair for Aggregate Constraints | 2026 | VLDB | 4.1905499e-05 |
| 10,825 | APEX-DAG: Library and Language independent Pipeline EXtraction | 2025 | VLDB | 4.1905499e-05 |
| 11,315 | Towards Observability for Machine Learning Pipelines | 2022 | CIDR | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,406 | Responsible Data Management | 2020 | VLDB | 0.0001216385 |
| 6,290 | Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines | 2021 | CIDR | 5.1220786e-05 |
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