Differential Data Quality Verification on Partitioned Data. - Sebastian Schelter; Stefan Grafberger; Philipp Schmidt 002; Tammo Rukat; Mario Kießling; Andrey Taptunov; Felix Bießmann; Dustin Lange (ICDE, 2019)

Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines. - Stefan Grafberger; Julia Stoyanovich; Sebastian Schelter (CIDR, 2021)

MLINSPECT: A Data Distribution Debugger for Machine Learning Pipelines. - Stefan Grafberger; Shubha Guha; Julia Stoyanovich; Sebastian Schelter (SIGMOD Conference, 2021)

HedgeCut: Maintaining Randomised Trees for Low-Latency Machine Unlearning. - Sebastian Schelter; Stefan Grafberger; Ted Dunning (SIGMOD Conference, 2021)

Screening Native Machine Learning Pipelines with ArgusEyes. - Sebastian Schelter; Stefan Grafberger; Shubha Guha; Olivier Sprangers; Bojan Karlas; Ce Zhang 001 (CIDR, 2022)

Towards data-centric what-if analysis for native machine learning pipelines. - Stefan Grafberger; Paul Groth; Sebastian Schelter (DEEM@SIGMOD, 2022)

Data distribution debugging in machine learning pipelines. - Stefan Grafberger; Paul Groth; Julia Stoyanovich; Sebastian Schelter (VLDB J., 2022)

schelterlabs/arguseyes. - Sebastian Schelter; Stefan Grafberger; Shubha Guha; Olivier Sprangers; Bojan Karlas; Ce Zhang 001 (Zenodo, 2022)

Proactively Screening Machine Learning Pipelines with ARGUSEYES. - Sebastian Schelter; Stefan Grafberger; Shubha Guha; Bojan Karlas; Ce Zhang 001 (SIGMOD Conference Companion, 2023)

Provenance Tracking for End-to-End Machine Learning Pipelines. - Stefan Grafberger; Paul Groth; Sebastian Schelter (WWW (Companion Volume), 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)

Improving Retrieval-Augmented Large Language Models via Data Importance Learning. - Xiaozhong Lyu; Stefan Grafberger; Samantha Biegel; Shaopeng Wei; Meng Cao; Sebastian Schelter; Ce Zhang 001 (CoRR, 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)

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)

shubhaguha/mlwhatif-demo: Demo for VLDB 2023. - Stefan Grafberger; Shubha Guha; Paul Groth; Sebastian Schelter (Zenodo, 2023)

Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines". - Stefan Grafberger; Paul Groth; Sebastian Schelter (DEEM@SIGMOD, 2024)

Instrumentation and Analysis of Native ML Pipelines via Logical Query Plans. - Stefan Grafberger (CoRR, 2024)

Messy Code Makes Managing ML Pipelines Difficult? Just Let LLMs Rewrite the Code! - Sebastian Schelter; Stefan Grafberger (CoRR, 2024)

Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All? - Rana Alotaibi; Yuanyuan Tian 001; Stefan Grafberger; Jesús Camacho-Rodríguez; Nicolas Bruno; Brian Kroth; Sergiy Matusevych; Ashvin Agrawal; Mahesh Behera; Ashit Gosalia; César A. Galindo-Legaria; Milind Joshi; et al. (CoRR, 2024)

Red Onions, Soft Cheese and Data: From Food Safety to Data Traceability for Responsible AI. - Stefan Grafberger; Zeyu Zhang; Sebastian Schelter; Ce Zhang 001 (IEEE Data Eng. Bull., 2024)

Snapcase - Regain Control over Your Predictions with Low-Latency Machine Unlearning. - Sebastian Schelter; Stefan Grafberger; Maarten de Rijke (Proc. VLDB Endow., 2024)

Automated Provenance-Based Screening of ML Data Preparation Pipelines. - Sebastian Schelter; Shubha Guha; Stefan Grafberger (Datenbank-Spektrum, 2024)

Ninth Workshop on Data Management for End-to-End Machine Learning (DEEM). - Stefan Grafberger; Madelon Hulsebos; Matteo Interlandi; Shreya Shankar (SIGMOD Conference Companion, 2025)