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Michael Hagmann
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2020 – today
- 2024
- [i5]Michael Staniek, Marius Fracarolli, Michael Hagmann, Stefan Riezler:
Early Prediction of Causes (not Effects) in Healthcare by Long-Term Clinical Time Series Forecasting. CoRR abs/2408.03816 (2024) - 2023
- [b2]Michael Hagmann:
Comprehensive Evaluation of Machine Learning Experiments: Algorithm Comparison, Algorithm Performance and Inferential Reproducibility. University of Heidelberg, Germany, 2023 - [c2]Michael Hagmann, Philipp Meier, Stefan Riezler:
Towards Inferential Reproducibility of Machine Learning Research. ICLR 2023 - [i4]Michael Hagmann, Stefan Riezler:
Towards Inferential Reproducibility of Machine Learning Research. CoRR abs/2302.04054 (2023) - [i3]Michael Hagmann, Shigehiko Schamoni, Stefan Riezler:
Validity problems in clinical machine learning by indirect data labeling using consensus definitions. CoRR abs/2311.03037 (2023) - 2022
- [c1]Shigehiko Schamoni, Michael Hagmann, Stefan Riezler:
Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis. MLHC 2022: 123-145 - [i2]Shigehiko Schamoni, Michael Hagmann, Stefan Riezler:
Ensembling Neural Networks for Improved Prediction and Privacy in Early Diagnosis of Sepsis. CoRR abs/2209.00439 (2022) - 2021
- [b1]Stefan Riezler, Michael Hagmann:
Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science. Synthesis Lectures on Human Language Technologies, Morgan & Claypool Publishers 2021, ISBN 978-3-031-01055-2, pp. 1-165 - [i1]Michael Hagmann, Stefan Riezler:
False perfection in machine prediction: Detecting and assessing circularity problems in machine learning. CoRR abs/2106.12417 (2021)
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