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2020 – today
- 2024
[c14]Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner:
Is this model reliable for everyone? Testing for strong calibration. AISTATS 2024: 181-189- [c13]Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio:
Monitoring machine learning-based risk prediction algorithms in the presence of performativity. AISTATS 2024: 919-927 - [c12]Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Anthony Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia:
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens. CLeaR 2024: 587-608 - [c11]Harvineet Singh, Fan Xia, Adarsh Subbaswamy, Alexej Gossmann, Jean Feng:
A hierarchical decomposition for explaining ML performance discrepancies. NeurIPS 2024 - [i10]Jean Feng, Harvineet Singh, Fan Xia, Adarsh Subbaswamy, Alexej Gossmann:
A hierarchical decomposition for explaining ML performance discrepancies. CoRR abs/2402.14254 (2024) - [i9]Xudong Sun, Nutan Chen, Alexej Gossmann, Yu Xing, Carla Feistner, Emilio Dorigatt, Felix Drost, Daniele Scarcella, Lisa Beer, Carsten Marr:
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling. CoRR abs/2403.13728 (2024) - [i8]Xudong Sun, Carla Feistner, Alexej Gossmann, George Schwarz, Rao Muhammad Umer, Lisa Beer, Patrick Rockenschaub, Rahul Babu Shrestha, Armin Gruber, Nutan Chen, Sayedali Shetab Boushehri, Florian Buettner, Carsten Marr:
DomainLab: A modular Python package for domain generalization in deep learning. CoRR abs/2403.14356 (2024) - 2023
- [c10]Tao Xu
, Fei Wang, Prithwish Chakraborty, Pei-Yun Sabrina Hsueh, Gregor Stiglic, Jiang Bian, Lixia Yao, Alexej Gossmann, Florian Buettner:
Workshop on Applied Data Science for Healthcare: Applications and New Frontiers of Generative Models for Healthcare. KDD 2023: 5893-5894 - [c9]Mariia Sidulova, Xudong Sun, Alexej Gossmann:
Deep Unsupervised Clustering for Conditional Identification of Subgroups Within a Digital Pathology Image Set. MICCAI (8) 2023: 666-675 - [i7]Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner:
Is this model reliable for everyone? Testing for strong calibration. CoRR abs/2307.15247 (2023) - [i6]Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia:
Towards a Post-Market Monitoring Framework for Machine Learning-based Medical Devices: A case study. CoRR abs/2311.11463 (2023) - 2022
- [j6]Jean Feng, Alexej Gossmann, Berkman Sahiner, Romain Pirracchio:
Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees. J. Am. Medical Informatics Assoc. 29(5): 841-852 (2022) - [c8]Tao Xu, Fei Wang, Prithwish Chakraborty, Pei-Yun Sabrina Hsueh, Gregor Stiglic, Jiang Bian, Lixia Yao, Alexej Gossmann, Florian Buettner:
Workshop on Applied Data Science for Healthcare (DSHealth): Transparent and Human-centered AI. KDD 2022: 4908-4909 - [c7]Jean Feng, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio, Alexej Gossmann:
Sequential algorithmic modification with test data reuse. UAI 2022: 674-684 - [i5]Jean Feng, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio, Alexej Gossmann:
Sequential algorithmic modification with test data reuse. CoRR abs/2203.11377 (2022) - [i4]Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio:
Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions. CoRR abs/2211.09781 (2022) - 2021
- [j5]Alexej Gossmann, Aria Pezeshk, Yu-Ping Wang, Berkman Sahiner:
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. 3(2): 692-714 (2021) - [i3]Jean Feng, Alexej Gossmann, Berkman Sahiner, Romain Pirracchio:
Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees. CoRR abs/2110.06866 (2021) - 2020
- [j4]Peyman Hosseinzadeh Kassani, Alexej Gossmann, Yu-Ping Wang:
Multimodal Sparse Classifier for Adolescent Brain Age Prediction. IEEE J. Biomed. Health Informatics 24(2): 336-344 (2020) - [c6]Alexej Gossmann, Kenny H. Cha, Xudong Sun:
Performance deterioration of deep neural networks for lesion classification in mammography due to distribution shift: an analysis based on artificially created distribution shift. Computer-Aided Diagnosis 2020 - [c5]Kenny H. Cha, Alexej Gossmann, Nicholas Petrick, Berkman Sahiner:
Supplementing training with data from a shifted distribution for machine learning classifiers: adding more cases may not always help. Image Perception, Observer Performance, and Technology Assessment 2020: 113160S
2010 – 2019
- 2019
- [c4]Xudong Sun, Alexej Gossmann, Yu Wang, Bernd Bischl:
Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift. SSCI 2019: 1344-1353 - [i2]Peyman Hosseinzadeh Kassani, Alexej Gossmann, Yu-Ping Wang:
Multimodal Sparse Classifier for Adolescent Brain Age Prediction. CoRR abs/1904.01070 (2019) - [i1]Xudong Sun, Yu Wang, Alexej Gossmann, Bernd Bischl:
Resampling-based Assessment of Robustness to Distribution Shift for Deep Neural Networks. CoRR abs/1906.02972 (2019) - 2018
- [j3]Alexej Gossmann, Shaolong Cao, Damian Brzyski, Lan-Juan Zhao, Hong-Wen Deng, Yu-Ping Wang:
A Sparse Regression Method for Group-Wise Feature Selection with False Discovery Rate Control. IEEE ACM Trans. Comput. Biol. Bioinform. 15(4): 1066-1078 (2018) - [j2]Alexej Gossmann, Pascal Zille, Vince D. Calhoun, Yu-Ping Wang:
FDR-Corrected Sparse Canonical Correlation Analysis With Applications to Imaging Genomics. IEEE Trans. Medical Imaging 37(8): 1761-1774 (2018) - [c3]Alexej Gossmann, Aria Pezeshk, Berkman Sahiner:
Test data reuse for evaluation of adaptive machine learning algorithms: over-fitting to a fixed 'test' dataset and a potential solution. Image Perception, Observer Performance, and Technology Assessment 2018: 105770K - 2016
- [j1]Shaolong Cao, Huaizhen Qin, Alexej Gossmann, Hong-Wen Deng, Yu-Ping Wang:
Unified tests for fine-scale mapping and identifying sparse high-dimensional sequence associations. Bioinform. 32(3): 330-337 (2016) - 2015
- [c2]Alexej Gossmann, Shaolong Cao, Yu-Ping Wang:
Identification of significant genetic variants via SLOPE, and its extension to group SLOPE. BCB 2015: 232-240 - [c1]Shaolong Cao, Huaizhen Qin, Alexej Gossmann, Hong-Wen Deng, Yu-Ping Wang:
Unified tests for fine scale mapping and identifying sparse high-dimensional sequence associations. BCB 2015: 241-249
Coauthor Index
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