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 | 65 | 2021 |
Lightweight Inspection of Data Preprocessing in Native Machine Learning Pipelines S Grafberger, J Stoyanovich, S Schelter Conference on Innovative Data Systems Research (CIDR), 2021 | 37 | 2021 |
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 | 26 | 2021 |
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 | 20 | 2018 |
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 | 19 | 2019 |
Data distribution debugging in machine learning pipelines S Grafberger, P Groth, J Stoyanovich, S Schelter The VLDB Journal 31 (5), 1103-1126, 2022 | 15 | 2022 |
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 | 11 | 2022 |
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 | 6 | 2023 |
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 | 6 | 2023 |
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 | 5 | 2023 |
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 | 4 | 2022 |
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 | 3 | 2023 |
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 | 3 | 2023 |
Towards Declarative Systems for Data-Centric Machine Learning S Grafberger, B Karlaš, P Groth, S Schelter | 1 | 2023 |
Towards Interactively Improving ML Data Preparation Code via" Shadow Pipelines" S Grafberger, P Groth, S Schelter arXiv preprint arXiv:2404.19591, 2024 | | 2024 |
Red Onions, Soft Cheese and Data: From Food Safety to Data Traceability for Responsible AI S Grafberger, Z Zhang, S Schelter, C Zhang Data Engineering, 63, 2024 | | 2024 |
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 |