Alexej Gossmann
Orcid: 0000-0001-9068-3877
According to our database1,
Alexej Gossmann
authored at least 22 papers
between 2015 and 2024.
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Bibliography
2024
CoRR, 2024
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling.
CoRR, 2024
2023
Towards a Post-Market Monitoring Framework for Machine Learning-based Medical Devices: A case study.
CoRR, 2023
Deep Unsupervised Clustering for Conditional Identification of Subgroups Within a Digital Pathology Image Set.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023
Workshop on Applied Data Science for Healthcare: Applications and New Frontiers of Generative Models for Healthcare.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
2022
Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees.
J. Am. Medical Informatics Assoc., 2022
Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions.
CoRR, 2022
Proceedings of the Uncertainty in Artificial Intelligence, 2022
Workshop on Applied Data Science for Healthcare (DSHealth): Transparent and Human-centered AI.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022
2021
Test Data Reuse for the Evaluation of Continuously Evolving Classification Algorithms Using the Area under the Receiver Operating Characteristic Curve.
SIAM J. Math. Data Sci., 2021
2020
IEEE J. Biomed. Health Informatics, 2020
Supplementing training with data from a shifted distribution for machine learning classifiers: adding more cases may not always help.
Proceedings of the Medical Imaging 2020: Image Perception, 2020
Performance deterioration of deep neural networks for lesion classification in mammography due to distribution shift: an analysis based on artificially created distribution shift.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
2019
Resampling-based Assessment of Robustness to Distribution Shift for Deep Neural Networks.
CoRR, 2019
Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2019
2018
FDR-Corrected Sparse Canonical Correlation Analysis With Applications to Imaging Genomics.
IEEE Trans. Medical Imaging, 2018
A Sparse Regression Method for Group-Wise Feature Selection with False Discovery Rate Control.
IEEE ACM Trans. Comput. Biol. Bioinform., 2018
Test data reuse for evaluation of adaptive machine learning algorithms: over-fitting to a fixed 'test' dataset and a potential solution.
Proceedings of the Medical Imaging 2018: Image Perception, 2018
2016
Unified tests for fine-scale mapping and identifying sparse high-dimensional sequence associations.
Bioinform., 2016
2015
Identification of significant genetic variants via SLOPE, and its extension to group SLOPE.
Proceedings of the 6th ACM Conference on Bioinformatics, 2015