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Found 12 papers, 8 papers with code
DomainLab: A modular Python package for domain generalization in deep learning
DomainLab is a modular Python package for training user specified neural networks with composable regularization loss terms.
M-HOF-Opt: Multi-Objective Hierarchical Output Feedback Optimization via Multiplier Induced Loss Landscape Scheduling
We address the online combinatorial choice of weight multipliers for multi-objective optimization of many loss terms parameterized by neural works via a probabilistic graphical model (PGM) for the joint model parameter and multiplier evolution process, with a hypervolume based likelihood promoting multi-objective descent.
A hierarchical decomposition for explaining ML performance discrepancies
Machine learning (ML) algorithms can often differ in performance across domains.
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens
When an ML algorithm interacts with its environment, the algorithm can affect the data-generating mechanism and be a major source of bias when evaluating its standalone performance, an issue known as performativity.
Is this model reliable for everyone? Testing for strong calibration
In a well-calibrated risk prediction model, the average predicted probability is close to the true event rate for any given subgroup.
Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions
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 predicts a patient to be at high risk for an adverse event, clinicians are more likely to administer prophylactic treatment and alter the very target that the algorithm aims to predict.
Sequential algorithmic modification with test data reuse
Each modification introduces a risk of deteriorating performance and must be validated on a test dataset.
Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees
In the COPD study, BLR and MarBLR dynamically combined the original model with a continually-refitted gradient boosted tree to achieve aAUCs of 0. 924 (95%CI 0. 913-0. 935) and 0. 925 (95%CI 0. 914-0. 935), compared to the static model's aAUC of 0. 904 (95%CI 0. 892-0. 916).
Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift
A novel variational inference based resampling framework is proposed to evaluate the robustness and generalization capability of deep learning models with respect to distribution shift.
Multimodal Sparse Classifier for Adolescent Brain Age Prediction
The study of healthy brain development helps to better understand the brain transformation and brain connectivity patterns which happen during childhood to adulthood.
FDR-Corrected Sparse Canonical Correlation Analysis with Applications to Imaging Genomics
Here we propose a way of applying the FDR concept to sparse CCA, and a method to control the FDR.
Group SLOPE - adaptive selection of groups of predictors
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.
Methodology 46N10 G.1.6
