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.
Code for our paper analyzing the looseness of the upper bound on selective classification performance.
Investigation of how sampling strategies affect Selective Prediction performance in Multi Task Learning
Deepfake detection with Bayesian uncertainty quantification, selective prediction, and an interactive Streamlit demo.
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
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