False perfection in machine prediction: Detecting and assessing circularity problems in machine learning. - Michael Hagmann; Stefan Riezler (CoRR, 2021)

Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science - Stefan Riezler; Michael Hagmann (Morgan & Claypool Publishers, 2021)

Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis. - Shigehiko Schamoni; Michael Hagmann; Stefan Riezler (MLHC, 2022)

Towards Inferential Reproducibility of Machine Learning Research. - Michael Hagmann; Philipp Meier; Stefan Riezler (ICLR, 2023)

Validity problems in clinical machine learning by indirect data labeling using consensus definitions. - Michael Hagmann; Shigehiko Schamoni; Stefan Riezler (CoRR, 2023)

Comprehensive Evaluation of Machine Learning Experiments: Algorithm Comparison, Algorithm Performance and Inferential Reproducibility. - Michael Hagmann (2023)

Early Prediction of Causes (not Effects) in Healthcare by Long-Term Clinical Time Series Forecasting. - Michael Staniek; Marius Fracarolli; Michael Hagmann; Stefan Riezler (MLHC, 2024)

Compositionality in Time Series: A Proof of Concept using Symbolic Dynamics and Compositional Data Augmentation. - Michael Hagmann; Michael Staniek; Stefan Riezler (Trans. Mach. Learn. Res., 2025)