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Learning Prediction Intervals for Model Performance
We address this core problem of performance prediction uncertainty with a method to compute prediction intervals for model performance.
Not Your Grandfathers Test Set: Reducing Labeling Effort for Testing
Building and maintaining high-quality test sets remains a laborious and expensive task.
Towards Automating the AI Operations Lifecycle
Today's AI deployments often require significant human involvement and skill in the operational stages of the model lifecycle, including pre-release testing, monitoring, problem diagnosis and model improvements.
Exploring the Hyperparameter Landscape of Adversarial Robustness
We present a sensitivity analysis that illustrates that the effectiveness of adversarial training hinges on the settings of a few salient hyperparameters.
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