Identification of significant genetic variants via SLOPE, and its extension to group SLOPE. - Alexej Gossmann; Shaolong Cao; Yu-Ping Wang 002 (BCB, 2015)

Unified tests for fine-scale mapping and identifying sparse high-dimensional sequence associations. - Shaolong Cao; Huaizhen Qin; Alexej Gossmann; Hong-Wen Deng; Yu-Ping Wang 002 (Bioinform., 2016)

Test data reuse for evaluation of adaptive machine learning algorithms: over-fitting to a fixed 'test' dataset and a potential solution. - Alexej Gossmann; Aria Pezeshk; Berkman Sahiner (Image Perception, Observer Performance, and Technology Assessment, 2018)

A Sparse Regression Method for Group-Wise Feature Selection with False Discovery Rate Control. - Alexej Gossmann; Shaolong Cao; Damian Brzyski; Lan-Juan Zhao; Hong-Wen Deng; Yu-Ping Wang 002 (IEEE ACM Trans. Comput. Biol. Bioinform., 2018)

FDR-Corrected Sparse Canonical Correlation Analysis With Applications to Imaging Genomics. - Alexej Gossmann; Pascal Zille; Vince D. Calhoun; Yu-Ping Wang 002 (IEEE Trans. Medical Imaging, 2018)

Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift. - Xudong Sun 002; Alexej Gossmann; Yu Wang; Bernd Bischl (SSCI, 2019)

Resampling-based Assessment of Robustness to Distribution Shift for Deep Neural Networks. - Xudong Sun 002; Yu Wang; Alexej Gossmann; Bernd Bischl (CoRR, 2019)

Performance deterioration of deep neural networks for lesion classification in mammography due to distribution shift: an analysis based on artificially created distribution shift. - Alexej Gossmann; Kenny H. Cha; Xudong Sun 002 (Computer-Aided Diagnosis, 2020)

Supplementing training with data from a shifted distribution for machine learning classifiers: adding more cases may not always help. - Kenny H. Cha; Alexej Gossmann; Nicholas Petrick; Berkman Sahiner (Image Perception, Observer Performance, and Technology Assessment, 2020)

Multimodal Sparse Classifier for Adolescent Brain Age Prediction. - Peyman Hosseinzadeh Kassani; Alexej Gossmann; Yu-Ping Wang 002 (IEEE J. Biomed. Health Informatics, 2020)

Test Data Reuse for the Evaluation of Continuously Evolving Classification Algorithms Using the Area under the Receiver Operating Characteristic Curve. - Alexej Gossmann; Aria Pezeshk; Yu-Ping Wang 002; Berkman Sahiner (SIAM J. Math. Data Sci., 2021)

Workshop on Applied Data Science for Healthcare (DSHealth): Transparent and Human-centered AI. - Tao Xu; Fei Wang 001; Prithwish Chakraborty; Pei-Yun Sabrina Hsueh; Gregor Stiglic; Jiang Bian 001; Lixia Yao; Alexej Gossmann; Florian Buettner 001 (KDD, 2022)

Sequential algorithmic modification with test data reuse. - Jean Feng; Gene Pennello; Nicholas Petrick; Berkman Sahiner; Romain Pirracchio; Alexej Gossmann (UAI, 2022)

Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions. - Jean Feng; Alexej Gossmann; Gene Pennello; Nicholas Petrick; Berkman Sahiner; Romain Pirracchio (CoRR, 2022)

Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees. - Jean Feng; Alexej Gossmann; Berkman Sahiner; Romain Pirracchio (J. Am. Medical Informatics Assoc., 2022)

Workshop on Applied Data Science for Healthcare: Applications and New Frontiers of Generative Models for Healthcare. - Tao Xu; Fei Wang 001; Prithwish Chakraborty; Pei-Yun Sabrina Hsueh; Gregor Stiglic; Jiang Bian 001; Lixia Yao; Alexej Gossmann; Florian Buettner 001 (KDD, 2023)

Deep Unsupervised Clustering for Conditional Identification of Subgroups Within a Digital Pathology Image Set. - Mariia Sidulova; Xudong Sun 002; Alexej Gossmann (MICCAI (8), 2023)

Towards a Post-Market Monitoring Framework for Machine Learning-based Medical Devices: A case study. - Jean Feng; Adarsh Subbaswamy; Alexej Gossmann; Harvineet Singh; Berkman Sahiner; Mi-Ok Kim; Gene Pennello; Nicholas Petrick; Romain Pirracchio; Fan Xia (CoRR, 2023)

Is this model reliable for everyone? Testing for strong calibration. - Jean Feng; Alexej Gossmann; Romain Pirracchio; Nicholas Petrick; Gene Pennello; Berkman Sahiner (AISTATS, 2024)

Monitoring machine learning-based risk prediction algorithms in the presence of performativity. - Jean Feng; Alexej Gossmann; Gene Pennello; Nicholas Petrick; Berkman Sahiner; Romain Pirracchio (AISTATS, 2024)

Contextual Unsupervised Deep Clustering in Digital Pathology. - Mariia Sidulova; Seyed Kahaki; Ian S. Hagemann; Alexej Gossmann (CHIL, 2024)

Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens. - Jean Feng; Adarsh Subbaswamy; Alexej Gossmann; Harvineet Singh; Berkman Sahiner; Mi-Ok Kim; Gene Anthony Pennello; Nicholas Petrick; Romain Pirracchio; Fan Xia (CLeaR, 2024)

A hierarchical decomposition for explaining ML performance discrepancies. - Harvineet Singh; Fan Xia; Adarsh Subbaswamy; Alexej Gossmann; Jean Feng (NeurIPS, 2024)

M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling. - Xudong Sun 002; Nutan Chen; Alexej Gossmann; Yu Xing; Carla Feistner; Emilio Dorigatt; Felix Drost; Daniele Scarcella; Lisa Beer; Carsten Marr (CoRR, 2024)

DomainLab: A modular Python package for domain generalization in deep learning. - Xudong Sun 002; 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 001; et al. (CoRR, 2024)

M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling. - Xudong Sun 002; Nutan Chen; Alexej Gossmann; Yu Xing; Matteo Wohlrapp; Emilio Dorigatti; Carla Feistner; Felix Drost; Daniele Scarcella; Lisa Beer; Carsten Marr (AISTATS, 2025)

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift. - Harvineet Singh; Fan Xia; Alexej Gossmann; Andrew Chuang; Julian C. Hong; Jean Feng (CoRR, 2025)