Stefan Grafberger

According to our database, Stefan Grafberger authored at least 20 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Snapcase - Regain Control over Your Predictions with Low-Latency Machine Unlearning.

Sebastian Schelter, Stefan Grafberger, Maarten de Rijke

Proc. VLDB Endow., August, 2024

Red Onions, Soft Cheese and Data: From Food Safety to Data Traceability for Responsible AI.

Stefan Grafberger, Zeyu Zhang, Sebastian Schelter, Ce Zhang

IEEE Data Eng. Bull., 2024

Messy Code Makes Managing ML Pipelines Difficult? Just Let LLMs Rewrite the Code!

Sebastian Schelter, Stefan Grafberger

CoRR, 2024

Instrumentation and Analysis of Native ML Pipelines via Logical Query Plans.

Stefan Grafberger

CoRR, 2024

Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines".

Stefan Grafberger, Paul Groth, Sebastian Schelter

Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning, 2024

2023

shubhaguha/mlwhatif-demo: Demo for VLDB 2023.

Stefan Grafberger, Shubha Guha, Paul Groth, Sebastian Schelter

Dataset, July, 2023

MLWHATIF: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses Over and Over?

Stefan Grafberger, Shubha Guha, Paul Groth, Sebastian Schelter

Proc. VLDB Endow., 2023

Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines.

Stefan Grafberger, Paul Groth, Sebastian Schelter

Proc. ACM Manag. Data, 2023

Improving Retrieval-Augmented Large Language Models via Data Importance Learning.

Xiaozhong Lyu, Stefan Grafberger, Samantha Biegel, Shaopeng Wei, Meng Cao, Sebastian Schelter, Ce Zhang

CoRR, 2023

How to Compliment a Human - Designing Affective and Well-being Promoting Conversational Things.

Ilhan Aslan, Dominik Neu, Daniela Neupert, Stefan Grafberger, Nico Weise, Pascal Pfeil, Maximilian Kuschewski

CoRR, 2023

Provenance Tracking for End-to-End Machine Learning Pipelines.

Stefan Grafberger, Paul Groth, Sebastian Schelter

Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 2023

Proactively Screening Machine Learning Pipelines with ARGUSEYES.

Sebastian Schelter, Stefan Grafberger, Shubha Guha, Bojan Karlas, Ce Zhang

Proceedings of the Companion of the 2023 International Conference on Management of Data, 2023

2022

schelterlabs/arguseyes.

Sebastian Schelter, Stefan Grafberger, Shubha Guha, Olivier Sprangers, Bojan Karlas, Ce Zhang

Dataset, January, 2022

Data distribution debugging in machine learning pipelines.

Stefan Grafberger, Paul Groth, Julia Stoyanovich, Sebastian Schelter

VLDB J., 2022

Towards data-centric what-if analysis for native machine learning pipelines.

Stefan Grafberger, Paul Groth, Sebastian Schelter

Proceedings of the DEEM '22: Proceedings of the Sixth Workshop on Data Management for End-To-End Machine Learning Philadelphia, 2022

Screening Native Machine Learning Pipelines with ArgusEyes.

Sebastian Schelter, Stefan Grafberger, Shubha Guha, Olivier Sprangers, Bojan Karlas, Ce Zhang

Proceedings of the 12th Conference on Innovative Data Systems Research, 2022

2021

HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning.

Sebastian Schelter, Stefan Grafberger, Ted Dunning

Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines.

Stefan Grafberger, Shubha Guha, Julia Stoyanovich, Sebastian Schelter

Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines.

Stefan Grafberger, Julia Stoyanovich, Sebastian Schelter

Proceedings of the 11th Conference on Innovative Data Systems Research, 2021

2019

Differential Data Quality Verification on Partitioned Data.

Sebastian Schelter, Stefan Grafberger, Philipp Schmidt, Tammo Rukat, Mario Kießling, Andrey Taptunov, Felix Bießmann, Dustin Lange

Proceedings of the 35th IEEE International Conference on Data Engineering, 2019