mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses Over and Over?
Summary: mlwhatif declaratively specifies data-centric what-if analyses over existing ML pipelines, automatically generating and optimizing pipeline variants. It supports robustness to data errors, cleaning impact, and preprocessing effects on fairness without reimplementation. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Stefan Grafberger (University of Amsterdam)
- 2. Shubha Guha (University of Amsterdam)
- 3. Paul Groth (University of Amsterdam)
- 4. Sebastian Schelter (University of Amsterdam)
BibTeX Citation
@article{grafberger_vldb23,
title = {{mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses Over and Over?}},
author = {Grafberger, Stefan and Guha, Shubha and Groth, Paul and Schelter, Sebastian},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {4002--4005},
doi = {10.14778/3611540.3611606},
url = {https://doi.org/10.14778/3611540.3611606},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,043 | Falcon: Fair Active Learning using Multi-armed Bandits | 2024 | VLDB | 5.5013766e-05 |
| 9,541 | Shapley Value Estimation Based on Differential Matrix | 2025 | SIGMOD | 5.2528121e-05 |
| 10,198 | ASSS: Adaptive Stratified Sampling for Shapley-like Values | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,066 | Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms | 2019 | VLDB | 0.00012333161 |
| 1,340 | Responsible Data Management | 2020 | VLDB | 0.00011111667 |
| 4,518 | MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines | 2021 | SIGMOD | 6.6474737e-05 |
| 7,395 | Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines | 2023 | SIGMOD | 5.6257796e-05 |
| 11,509 | Screening Native ML Pipelines with “ArgusEyes” | 2022 | CIDR | 5.093636e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 11,512 | Towards Observability for Machine Learning Pipelines | 2022 | CIDR |
| 2 | 9,245 | Towards Observability for Production Machine Learning Pipelines | 2022 | VLDB |
| 3 | 6,309 | Materialization and Reuse Optimizations for Production Data Science Pipelines | 2022 | SIGMOD |
| 4 | 8,177 | HAIPipe: Combining Human-generated and Machine-generated Pipelines for Data Preparation | 2023 | SIGMOD |
| 5 | 11,043 | mlidea: Interactively Improving ML Data Preparation Code via “Shadow Pipelines” | 2025 | VLDB |
| 6 | 2,657 | Production Machine Learning Pipelines: Empirical Analysis and Optimization Opportunities | 2021 | SIGMOD |
| 7 | 11,353 | Reconstructing and Querying ML Pipeline Intermediates | 2023 | CIDR |
| 8 | 6,250 | Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines | 2021 | CIDR |
| 9 | 4,518 | MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines | 2021 | SIGMOD |
| 10 | 7,395 | Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines | 2023 | SIGMOD |