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)

Paper ID: hdb2a4a516805d97b
Venue: VLDB
Year: 2023
Pagerank: 5.4803665e-05
Overall Rank: 7,629 | 48.71%
DOI: 10.14778/3611540.3611606

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
8,205	Falcon: Fair Active Learning using Multi-armed Bandits	2024	VLDB	5.3781556e-05
9,723	Shapley Value Estimation Based on Differential Matrix	2025	SIGMOD	5.1349531e-05
10,414	ASSS: Adaptive Stratified Sampling for Shapley-like Values	2026	SIGMOD	4.9793485e-05

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
894	Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms	2019	VLDB	0.00013212578
1,162	Responsible Data Management	2020	VLDB	0.00011753159
4,606	MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines	2021	SIGMOD	6.5041225e-05
7,538	Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines	2023	SIGMOD	5.4995874e-05
11,818	Screening Native ML Pipelines with “ArgusEyes”	2022	CIDR	4.9793485e-05

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