5 citations · 16 across the 13 of their papers we have counts for
13 papers
"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift
Harvineet Singh, Fan Xia, Alexej Gossmann +3
Machine learning (ML) models frequently experience performance degradation when deployed in new contexts. Such degradation is rarely uniform: some subgroups may suffer large perfor…
DomainLab: A modular Python package for domain generalization in deep learning
Xudong Sun, Carla Feistner, Alexej Gossmann +10
Poor generalization performance caused by distribution shifts in unseen domains often hinders the trustworthy deployment of deep neural networks. Many domain generalization techniq…
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
Xudong Sun, Nutan Chen, Alexej Gossmann +8
A probabilistic graphical model is proposed, modeling the joint model parameter and multiplier evolution, with a hypervolume based likelihood, promoting multi-objective descent in…
A hierarchical decomposition for explaining ML performance discrepancies
Jean Feng, Harvineet Singh, Fan Xia +2
Machine learning (ML) algorithms can often differ in performance across domains. Understanding their performance differs is crucial for determining what types of int…
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
Jean Feng, Adarsh Subbaswamy, Alexej Gossmann +7
After a machine learning (ML)-based system is deployed, monitoring its performance is important to ensure the safety and effectiveness of the algorithm over time. When an ML algori…
Is this model reliable for everyone? Testing for strong calibration
Jean Feng, Alexej Gossmann, Romain Pirracchio +3
In a well-calibrated risk prediction model, the average predicted probability is close to the true event rate for any given subgroup. Such models are reliable across heterogeneous…
Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions
Jean Feng, Alexej Gossmann, Gene Pennello +3
Performance monitoring of machine learning (ML)-based risk prediction models in healthcare is complicated by the issue of confounding medical interventions (CMI): when an algorithm…
Sequential algorithmic modification with test data reuse
Jean Feng, Gene Pennello, Nicholas Petrick +3
After initial release of a machine learning algorithm, the model can be fine-tuned by retraining on subsequently gathered data, adding newly discovered features, or more. Each modi…
Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees
Jean Feng, Alexej Gossmann, Berkman Sahiner +1
After deploying a clinical prediction model, subsequently collected data can be used to fine-tune its predictions and adapt to temporal shifts. Because model updating carries risks…
Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift
Xudong Sun, Alexej Gossmann, Yu Wang +1
A novel variational inference based resampling framework is proposed to evaluate the robustness and generalization capability of deep learning models with respect to distribution s…
Multimodal Sparse Classifier for Adolescent Brain Age Prediction
Peyman Hosseinzadeh Kassani, Alexej Gossmann, Yu-Ping Wang
The study of healthy brain development helps to better understand the brain transformation and brain connectivity patterns which happen during childhood to adulthood. This study pr…
FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics
Alexej Gossmann, Pascal Zille, Vince Calhoun +1
Reducing the number of false discoveries is presently one of the most pressing issues in the life sciences. It is of especially great importance for many applications in neuroimagi…
Group SLOPE - adaptive selection of groups of predictors
Damian Brzyski, Alexej Gossmann, Weijie Su +1
Sorted L-One Penalized Estimation (SLOPE) is a relatively new convex optimization procedure which allows for adaptive selection of regressors under sparse high dimensional designs.…