2 papers
cs.LG2026
SemPipes -- Optimizable Semantic Data Operators for Tabular Machine Learning Pipelines
Olga Ovcharenko, Matthias Boehm, Sebastian Schelter
Real-world machine learning on tabular data relies on complex data preparation pipelines for prediction, data integration, augmentation, and debugging. Designing these pipelines re…
cs.DB2025
Morphing-based Compression for Data-centric ML Pipelines
Sebastian Baunsgaard, Matthias Boehm
Data-centric ML pipelines extend traditional machine learning (ML) pipelines -- of feature transformations and ML model training -- by outer loops for data cleaning, augmentation,…