Author

J. V. Rijn

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Adaptive Data Profiling with ML-Based Quality Checks for ETL Pipeline Quality and Cost Optimization

This thesis investigates whether machine learning can automatically learn what normal data looks like and reliably flag deviations, serving as a low-cost complement to rule-based checks at the point where data first enters the pipeline, and whether AutoML-based anomaly detection is a practical complement to rule-based...

Koorosh Komeili Zadeh, Joost Visser, J. V. van Rijn · 0 citations

Preprint Aug 2026

Evolving Executable Pipeline Programs for AutoML with Language Models

This work presents LACE, an AutoML framework that instead searches over complete executable pipeline programs: an evolutionary loop maintains a population of scikit-learn-compatible Python classes, and a large language model acts as the variation operator.

Sofoklis Kitharidis, C. Veenman, J. V. van Rijn et al. · 0 citations