Derives simultaneous finite-sample distribution-free upper bounds on false discovery proportions for conformal p-values that hold for every possible rejection threshold.
Advances in neural information processing systems , volume=
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Multi-Beholder integrates one-class classification into multiple instance learning to predict LGG biomarker status from histopathology images, reporting AUCs of 0.973 on TCGA-LGG and 0.820 on an external Xiangya cohort.
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Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference
Derives simultaneous finite-sample distribution-free upper bounds on false discovery proportions for conformal p-values that hold for every possible rejection threshold.
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Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classification
Multi-Beholder integrates one-class classification into multiple instance learning to predict LGG biomarker status from histopathology images, reporting AUCs of 0.973 on TCGA-LGG and 0.820 on an external Xiangya cohort.