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Found 4 papers, 3 papers with code
Deep Neural Network Benchmarks for Selective Classification
The selective classification framework aims to design a mechanism that balances the fraction of rejected predictions (i. e., the proportion of examples for which the model does not make a prediction) versus the improvement in predictive performance on the selected predictions.
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly Detection
In this paper, we propose a mixed strategy that, given a budget of labels, decides in multiple rounds whether to use the budget to collect AL labels or LR labels.
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection
We leverage on outputs of several anomaly detectors as a representation that already captures the basic notion of anomalousness and estimate the contamination using a specific mixture formulation.
Machine Learning with a Reject Option: A survey
Machine learning models always make a prediction, even when it is likely to be inaccurate.
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