Alexej Gossmann

34 papers A* 4A 4Journal 17Unranked 9

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift. ICML Harvineet Singh, Fan Xia, Alexej Gossmann, Andrew Chuang, Julian C. Hong, Jean Feng
2025	J	`jnl`	"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift. CoRR Harvineet Singh, Fan Xia, Alexej Gossmann, Andrew Chuang, Julian C. Hong, Jean Feng
2025	A	`conf`	M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling. AISTATS Xudong Sun, Nutan Chen, Alexej Gossmann, Yu Xing, Matteo Wohlrapp, Emilio Dorigatti, Carla Feistner, Felix Drost, Daniele Scarcella, Lisa Beer, Carsten Marr
2024	A*	`conf`	A hierarchical decomposition for explaining ML performance discrepancies. NeurIPS Harvineet Singh, Fan Xia, Adarsh Subbaswamy, Alexej Gossmann, Jean Feng
2024	J	`jnl`	A hierarchical decomposition for explaining ML performance discrepancies. CoRR Jean Feng, Harvineet Singh, Fan Xia, Adarsh Subbaswamy, Alexej Gossmann
2024	—	`conf`	Contextual Unsupervised Deep Clustering in Digital Pathology. CHIL Mariia Sidulova, Seyed Kahaki, Ian S. Hagemann, Alexej Gossmann
2024	—	`conf`	Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens. CLeaR Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Anthony Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia
2024	J	`jnl`	DomainLab: A modular Python package for domain generalization in deep learning. CoRR 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
2024	A	`conf`	Is this model reliable for everyone? Testing for strong calibration. AISTATS Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner
2024	J	`jnl`	M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling. CoRR Xudong Sun, Nutan Chen, Alexej Gossmann, Yu Xing, Carla Feistner, Emilio Dorigatt, Felix Drost, Daniele Scarcella, Lisa Beer, Carsten Marr
2024	A	`conf`	Monitoring machine learning-based risk prediction algorithms in the presence of performativity. AISTATS Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio
2023	—	`conf`	Deep Unsupervised Clustering for Conditional Identification of Subgroups Within a Digital Pathology Image Set. MICCAI (8) Mariia Sidulova, Xudong Sun, Alexej Gossmann
2023	J	`jnl`	Is this model reliable for everyone? Testing for strong calibration. CoRR Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner
2023	J	`jnl`	Towards a Post-Market Monitoring Framework for Machine Learning-based Medical Devices: A case study. CoRR Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia
2023	A*	`conf`	Workshop on Applied Data Science for Healthcare: Applications and New Frontiers of Generative Models for Healthcare. KDD Tao Xu, Fei Wang, Prithwish Chakraborty, Pei-Yun Sabrina Hsueh, Gregor Stiglic, Jiang Bian, Lixia Yao, Alexej Gossmann, Florian Buettner
2022	J	`jnl`	Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees. J. Am. Medical Informatics Assoc. Jean Feng, Alexej Gossmann, Berkman Sahiner, Romain Pirracchio
2022	J	`jnl`	Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions. CoRR Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio
2022	A	`conf`	Sequential algorithmic modification with test data reuse. UAI Jean Feng, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio, Alexej Gossmann
2022	J	`jnl`	Sequential algorithmic modification with test data reuse. CoRR Jean Feng, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio, Alexej Gossmann
2022	A*	`conf`	Workshop on Applied Data Science for Healthcare (DSHealth): Transparent and Human-centered AI. KDD Tao Xu, Fei Wang, Prithwish Chakraborty, Pei-Yun Sabrina Hsueh, Gregor Stiglic, Jiang Bian, Lixia Yao, Alexej Gossmann, Florian Buettner
2021	J	`jnl`	Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees. CoRR Jean Feng, Alexej Gossmann, Berkman Sahiner, Romain Pirracchio
2021	J	`jnl`	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. Alexej Gossmann, Aria Pezeshk, Yu-Ping Wang, Berkman Sahiner
2020	J	`jnl`	Multimodal Sparse Classifier for Adolescent Brain Age Prediction. IEEE J. Biomed. Health Informatics Peyman Hosseinzadeh Kassani, Alexej Gossmann, Yu-Ping Wang
2020	—	`conf`	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 Alexej Gossmann, Kenny H. Cha, Xudong Sun
2020	—	`conf`	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 Kenny H. Cha, Alexej Gossmann, Nicholas Petrick, Berkman Sahiner
2019	J	`jnl`	Multimodal Sparse Classifier for Adolescent Brain Age Prediction. CoRR Peyman Hosseinzadeh Kassani, Alexej Gossmann, Yu-Ping Wang
2019	J	`jnl`	Resampling-based Assessment of Robustness to Distribution Shift for Deep Neural Networks. CoRR Xudong Sun, Yu Wang, Alexej Gossmann, Bernd Bischl
2019	—	`conf`	Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift. SSCI Xudong Sun, Alexej Gossmann, Yu Wang, Bernd Bischl
2018	J	`jnl`	A Sparse Regression Method for Group-Wise Feature Selection with False Discovery Rate Control. IEEE ACM Trans. Comput. Biol. Bioinform. Alexej Gossmann, Shaolong Cao, Damian Brzyski, Lan-Juan Zhao, Hong-Wen Deng, Yu-Ping Wang
2018	J	`jnl`	FDR-Corrected Sparse Canonical Correlation Analysis With Applications to Imaging Genomics. IEEE Trans. Medical Imaging Alexej Gossmann, Pascal Zille, Vince D. Calhoun, Yu-Ping Wang
2018	—	`conf`	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 Alexej Gossmann, Aria Pezeshk, Berkman Sahiner
2016	J	`jnl`	Unified tests for fine-scale mapping and identifying sparse high-dimensional sequence associations. Bioinform. Shaolong Cao, Huaizhen Qin, Alexej Gossmann, Hong-Wen Deng, Yu-Ping Wang
2015	—	`conf`	Identification of significant genetic variants via SLOPE, and its extension to group SLOPE. BCB Alexej Gossmann, Shaolong Cao, Yu-Ping Wang
2015	—	`conf`	Unified tests for fine scale mapping and identifying sparse high-dimensional sequence associations. BCB Shaolong Cao, Huaizhen Qin, Alexej Gossmann, Hong-Wen Deng, Yu-Ping Wang