Alexej Gossmann

Orcid: 0000-0001-9068-3877

According to our database1, Alexej Gossmann authored at least 24 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
DomainLab: A modular Python package for domain generalization in deep learning.
CoRR, 2024

M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling.
CoRR, 2024

A hierarchical decomposition for explaining ML performance discrepancies.
CoRR, 2024

Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens.
Proceedings of the Causal Learning and Reasoning, 2024

Monitoring machine learning-based risk prediction algorithms in the presence of performativity.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Is this model reliable for everyone? Testing for strong calibration.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 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

Sequential algorithmic modification with test data reuse.
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
Multimodal Sparse Classifier for Adolescent Brain Age Prediction.
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


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