OxEnsemble improves fairness-accuracy trade-offs in low-data medical imaging by training fairness-constrained ensemble members and aggregating predictions with theoretical guarantees.
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POTTERS extends the Potts model with generalized spatial dependence and external priors for Bayesian remote sensing image segmentation via variational inference, without needing target-region labels.
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OxEnsemble: Fair Ensembles for Low-Data Classification
OxEnsemble improves fairness-accuracy trade-offs in low-data medical imaging by training fairness-constrained ensemble members and aggregating predictions with theoretical guarantees.
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Scalable Bayesian Spatial Mixture Modelling for Remote Sensing Image Segmentation
POTTERS extends the Potts model with generalized spatial dependence and external priors for Bayesian remote sensing image segmentation via variational inference, without needing target-region labels.