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ORCIDStefan Grafberger
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2020 – today
- 2024
[j5]Stefan Grafberger, Zeyu Zhang, Sebastian Schelter, Ce Zhang:
Red Onions, Soft Cheese and Data: From Food Safety to Data Traceability for Responsible AI. IEEE Data Eng. Bull. 47(1): 63-81 (2024)- [j4]Sebastian Schelter, Stefan Grafberger, Maarten de Rijke:
Snapcase - Regain Control over Your Predictions with Low-Latency Machine Unlearning. Proc. VLDB Endow. 17(12): 4273-4276 (2024) - [c9]Stefan Grafberger
, Paul Groth, Sebastian Schelter:
Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines". DEEM@SIGMOD 2024: 7-11 - [i5]Stefan Grafberger, Paul Groth, Sebastian Schelter:
Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines". CoRR abs/2404.19591 (2024) - [i4]Stefan Grafberger:
Instrumentation and Analysis of Native ML Pipelines via Logical Query Plans. CoRR abs/2407.07560 (2024) - [i3]Sebastian Schelter, Stefan Grafberger:
Messy Code Makes Managing ML Pipelines Difficult? Just Let LLMs Rewrite the Code! CoRR abs/2409.10081 (2024) - 2023
- [j3]Stefan Grafberger, Paul Groth, Sebastian Schelter:
Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines. Proc. ACM Manag. Data 1(2): 128:1-128:26 (2023) - [j2]Stefan Grafberger, Shubha Guha, Paul Groth, Sebastian Schelter:
MLWHATIF: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses Over and Over? Proc. VLDB Endow. 16(12): 4002-4005 (2023) - [c8]Sebastian Schelter, Stefan Grafberger, Shubha Guha, Bojan Karlas, Ce Zhang:
Proactively Screening Machine Learning Pipelines with ARGUSEYES. SIGMOD Conference Companion 2023: 91-94 - [c7]Stefan Grafberger, Paul Groth, Sebastian Schelter:
Provenance Tracking for End-to-End Machine Learning Pipelines. WWW (Companion Volume) 2023: 1512 - [d2]Stefan Grafberger, Shubha Guha, Paul Groth, Sebastian Schelter:
shubhaguha/mlwhatif-demo: Demo for VLDB 2023. Zenodo, 2023
- [i2]Ilhan Aslan, Dominik Neu, Daniela Neupert, Stefan Grafberger, Nico Weise, Pascal Pfeil, Maximilian Kuschewski:
How to Compliment a Human - Designing Affective and Well-being Promoting Conversational Things. CoRR abs/2302.00984 (2023) - [i1]Xiaozhong Lyu, Stefan Grafberger, Samantha Biegel, Shaopeng Wei, Meng Cao, Sebastian Schelter, Ce Zhang:
Improving Retrieval-Augmented Large Language Models via Data Importance Learning. CoRR abs/2307.03027 (2023) - 2022
- [j1]Stefan Grafberger, Paul Groth, Julia Stoyanovich, Sebastian Schelter:
Data distribution debugging in machine learning pipelines. VLDB J. 31(5): 1103-1126 (2022) - [c6]Sebastian Schelter, Stefan Grafberger, Shubha Guha, Olivier Sprangers, Bojan Karlas, Ce Zhang:
Screening Native Machine Learning Pipelines with ArgusEyes. CIDR 2022 - [c5]Stefan Grafberger, Paul Groth, Sebastian Schelter:
Towards data-centric what-if analysis for native machine learning pipelines. DEEM@SIGMOD 2022: 3:1-3:5 - [d1]Sebastian Schelter, Stefan Grafberger, Shubha Guha, Olivier Sprangers, Bojan Karlas, Ce Zhang:
schelterlabs/arguseyes. Zenodo, 2022 - 2021
- [c4]Stefan Grafberger, Julia Stoyanovich, Sebastian Schelter:
Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines. CIDR 2021 - [c3]Sebastian Schelter, Stefan Grafberger, Ted Dunning:
HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning. SIGMOD Conference 2021: 1545-1557 - [c2]Stefan Grafberger, Shubha Guha, Julia Stoyanovich, Sebastian Schelter:
MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines. SIGMOD Conference 2021: 2736-2739
2010 – 2019
- 2019
- [c1]Sebastian Schelter, Stefan Grafberger, Philipp Schmidt, Tammo Rukat, Mario Kießling, Andrey Taptunov, Felix Bießmann, Dustin Lange:
Differential Data Quality Verification on Partitioned Data. ICDE 2019: 1940-1945
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