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Sebastian Schelter
- Author ID
- o0000-0003-4722-5840
- ORCID
-
0000-0003-4722-5840
- Links
-
(found by gpt-5.6-luna on jul 24 2026)
- Most Frequent Institution
- University of Amsterdam
- Pagerank
- 0.20065666
- Overall Rank
- 287 | 98.68%
- Paper Count
- 27
Affiliation Timeline
Incoming Non-self Citations Over Time
Total yearly non-self incoming citations across all papers by this author.
Publications by Paper Pagerank
Showing all 27 publications. Total citations include self and non-self citations.
| Rank |
Title |
Year |
Venue |
Total Citations |
Pagerank |
| 1,308 |
Automating Large-Scale Data Quality Verification |
2018 |
VLDB |
28 |
0.00011073863 |
| 1,928 |
Elastic Machine Learning Algorithms in Amazon SageMaker |
2020 |
SIGMOD |
20 |
9.3563363e-05 |
| 2,266 |
An Intermediate Representation for Optimizing Machine Learning Pipelines |
2019 |
VLDB |
21 |
8.7248802e-05 |
| 3,738 |
Learning to Validate the Predictions of Black Box Classifiers on Unseen Data |
2020 |
SIGMOD |
10 |
7.0609398e-05 |
| 4,039 |
HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning |
2021 |
SIGMOD |
7 |
6.835801e-05 |
| 4,608 |
MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines |
2021 |
SIGMOD |
9 |
6.5010466e-05 |
| 5,308 |
Probabilistic Demand Forecasting at Scale |
2017 |
VLDB |
14 |
6.1844285e-05 |
| 5,309 |
SemBench: A Benchmark for Semantic Query Processing Engines |
2026 |
VLDB |
10 |
6.1841176e-05 |
| 5,393 |
SchemaPile: A Large Collection of Relational Database Schemas |
2024 |
SIGMOD |
4 |
6.1494131e-05 |
| 6,325 |
"Amnesia" - A Selection of Machine Learning Models That Can Forget User Data Very Fast |
2020 |
CIDR |
6 |
5.8121503e-05 |
| 6,327 |
Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines |
2021 |
CIDR |
6 |
5.8111081e-05 |
| 6,900 |
Unit Testing Data with Deequ |
2019 |
SIGMOD |
4 |
5.6503837e-05 |
| 7,544 |
Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines |
2023 |
SIGMOD |
8 |
5.496984e-05 |
| 7,635 |
mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses Over and Over? |
2023 |
VLDB |
3 |
5.4777741e-05 |
| 8,316 |
DORIAN in action: Assisted Design of Data Science Pipelines |
2022 |
VLDB |
3 |
5.3556589e-05 |
| 9,716 |
Serenade - Low-Latency Session-Based Recommendation in e-Commerce at Scale |
2022 |
SIGMOD |
2 |
5.1347107e-05 |
| 9,829 |
BlockJoin: Efficient Matrix Partitioning Through Joins |
2017 |
VLDB |
2 |
5.1230568e-05 |
| 9,831 |
Optimistic Recovery for Iterative Dataflows in Action |
2015 |
SIGMOD |
2 |
5.1230071e-05 |
| 10,907 |
stratum: A System Infrastructure for Massive Agent-Centric ML Workloads |
2026 |
VLDB |
0 |
4.9769913e-05 |
| 10,970 |
SemPiper: Interactive Code Synthesis for Semantic Operators in Machine Learning Pipelines |
2026 |
VLDB |
0 |
4.9769913e-05 |
| 11,413 |
mlidea: Interactively Improving ML Data Preparation Code via “Shadow Pipelines” |
2025 |
VLDB |
0 |
4.9769913e-05 |
| 11,613 |
A Flexible Forecasting Stack |
2024 |
VLDB |
1 |
4.9769913e-05 |
| 11,627 |
Snapcase – Regain Control over Your Predictions with Low-Latency Machine Unlearning |
2024 |
VLDB |
0 |
4.9769913e-05 |
| 11,677 |
Reconstructing and Querying ML Pipeline Intermediates |
2023 |
CIDR |
0 |
4.9769913e-05 |
| 11,824 |
Screening Native ML Pipelines with “ArgusEyes” |
2022 |
CIDR |
3 |
4.9769913e-05 |
| 12,534 |
Iterative Parallel Data Processing with Stratosphere: An Inside Look |
2013 |
SIGMOD |
0 |
4.9769913e-05 |
| 13,820 |
DEEM 2019: Workshop on Data Management for End-to-End Machine Learning |
2019 |
SIGMOD |
0 |
- |
Frequent Co-authors
Co-authored at least 5 papers.