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Stefan Grafberger
Stefan Grafberger
Ph.D. Student at UvA
Verified email at uva.nl - Homepage
Title
Cited by
Cited by
Year
HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning
S Schelter, S Grafberger, T Dunning
Proceedings of the 2021 International Conference on Management of Data, 1545 …, 2021
612021
Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines
S Grafberger, J Stoyanovich, S Schelter
Conference on Innovative Data Systems Research (CIDR), 2021
372021
mlinspect: A Data Distribution Debugger for Machine Learning Pipelines
S Grafberger, S Guha, J Stoyanovich, S Schelter
Proceedings of the 2021 International Conference on Management of Data, 2736 …, 2021
232021
Differential Data Quality Verification on Partitioned Data
S Schelter, S Grafberger, P Schmidt, T Rukat, M Kiessling, A Taptunov, ...
2019 IEEE 35th International Conference on Data Engineering (ICDE), 1940-1945, 2019
182019
Deequ - Data Quality Validation for Machine Learning Pipelines
S Schelter, S Grafberger, P Schmidt, T Rukat, M Kiessling, A Taptunov, ...
Machine Learning Systems workshop at the conference on Neural Information …, 2018
182018
Data distribution debugging in machine learning pipelines
S Grafberger, P Groth, J Stoyanovich, S Schelter
The VLDB Journal 31 (5), 1103-1126, 2022
132022
Screening Native ML Pipelines with “ArgusEyes”
S Schelter, S Grafberger, S Guha, O Sprangers, B Karlaš, C Zhang
Conference on Innovative Data Systems Research. CIDR, 2022
92022
Improving Retrieval-Augmented Large Language Models via Data Importance Learning
X Lyu, S Grafberger, S Biegel, S Wei, M Cao, S Schelter, C Zhang
arXiv preprint arXiv:2307.03027, 2023
62023
Towards Data-Centric What-If Analysis for Native Machine Learning Pipelines
S Grafberger, P Groth, S Schelter
Proceedings of the Sixth Workshop on Data Management for End-To-End Machine …, 2022
42022
Automating and optimizing data-centric what-if analyses on native machine learning pipelines
S Grafberger, P Groth, S Schelter
Proceedings of the ACM on Management of Data 1 (2), 1-26, 2023
32023
Provenance tracking for end-to-end machine learning pipelines
S Grafberger, P Groth, S Schelter
Companion Proceedings of the ACM Web Conference 2023, 1512-1512, 2023
32023
Proactively Screening Machine Learning Pipelines with ArgusEyes
S Schelter, S Grafberger, S Guha, B Karlas, C Zhang
Companion of the 2023 International Conference on Management of Data, 91-94, 2023
22023
mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses over and over?
S Grafberger, S Guha, P Groth, S Schelter
Proceedings of the VLDB Endowment 16 (12), 4002-4005, 2023
12023
Towards Declarative Systems for Data-Centric Machine Learning
S Grafberger, B Karlaš, P Groth, S Schelter
2023
How to Compliment a Human - Designing Affective and Well-being Promoting Conversational Things
I Aslan, D Neu, D Neupert, S Grafberger, N Weise, P Pfeil, M Kuschewski
Interaction Design and Architecture(s) Journal - IxD&A 10.55612/s-5002-058-007, 2023
2023
ARGUSEYES: Screening Native Machine Learning Pipelines
S Grafberger, S Guha, O Sprangers, S Schelter
2021
mlinspect: Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines
S Grafberger, S Schelter
2020
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