Screening Native ML Pipelines with “ArgusEyes”
Summary: ArgusEyes: system-level screening for native ML pipelines that detects data-dependent and ecosystem-induced implementation faults by observing input-driven behavior and library/runtime interactions. Provides data scientists with fundamental support to catch correctness, robustness, and deployment regressions early. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Sebastian Schelter (University of Amsterdam)
- 2. Stefan Grafberger (University of Amsterdam)
- 3. Shubha Guha (University of Amsterdam)
- 4. Olivier Sprangers (University of Amsterdam)
- 5. Bojan Karlaš (ETH Zurich)
- 6. Ce Zhang (ETH Zurich)
BibTeX Citation
@inproceedings{schelter_cidr22,
address = {Amsterdam, Netherlands},
series = {{CIDR} '22},
title = {{Screening Native ML Pipelines with “ArgusEyes”}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Schelter, Sebastian and Grafberger, Stefan and Guha, Shubha and Sprangers, Olivier and Karlaš, Bojan and Zhang, Ce},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,395 | Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines | 2023 | SIGMOD | 5.6257796e-05 |
| 7,490 | mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses Over and Over? | 2023 | VLDB | 5.6061425e-05 |
| 8,244 | SHiFT: An Efficient, Flexible Search Engine for Transfer Learning | 2023 | VLDB | 5.4587712e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,340 | Responsible Data Management | 2020 | VLDB | 0.00011111667 |
| 6,188 | "Amnesia" - A Selection of Machine Learning Models That Can Forget User Data Very Fast | 2020 | CIDR | 5.9483532e-05 |
| 6,250 | Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines | 2021 | CIDR | 5.9418015e-05 |
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