We show that a model owner can artificially introduce uncertainty into their model and provide a corresponding detection mechanism.
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Updated
Jun 2, 2025 - Jupyter Notebook
We show that a model owner can artificially introduce uncertainty into their model and provide a corresponding detection mechanism.
When does suppressing low-confidence clinical alerts actually reduce alarm fatigue? Empirical two-regime analysis on PhysioNet 2019 sepsis data — ceiling effects, floor effects, and when neither applies.
Code for our paper analyzing the looseness of the upper bound on selective classification performance.
Deepfake detection with Bayesian uncertainty quantification, selective prediction, and an interactive Streamlit demo.
Investigation of how sampling strategies affect Selective Prediction performance in Multi Task Learning
BoundaryBench: Benchmark + tool-augmented method for boundary containment under GPS noise
Transform enrichment outputs into verifiable pathway claims via stability distillation, evidence modules, and mechanical PASS/ABSTAIN/FAIL audits.
Reproducible pipeline for silent-failure auditing in ECG accept-sets (MIT-BIH) with Newton–Puiseux onset scoring
Code Repository for SCoRE paper
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