10 citations · 25 across the 9 of their papers we have counts for
7 papers · 1 filter
Assessment of Prediction Intervals Using Uncertainty Characteristics Curves
Jiri Navratil, Benjamin Elder, Matthew Arnold +2
Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI. In regression tasks, uncertainty is typically quantified using pr…
Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals
Jiri Navratil, Benjamin Elder, Matthew Arnold +2
Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI. In regression tasks, uncertainty is typically quantified using pr…
Learning Prediction Intervals for Model Performance
Benjamin Elder, Matthew Arnold, Anupama Murthi +1
Understanding model performance on unlabeled data is a fundamental challenge of developing, deploying, and maintaining AI systems. Model performance is typically evaluated using te…
Not Your Grandfathers Test Set: Reducing Labeling Effort for Testing
Begum Taskazan, Jiri Navratil, Matthew Arnold +3
Building and maintaining high-quality test sets remains a laborious and expensive task. As a result, test sets in the real world are often not properly kept up to date and drift fr…
Uncertainty Prediction for Deep Sequential Regression Using Meta Models
Jiri Navratil, Matthew Arnold, Benjamin Elder
Generating high quality uncertainty estimates for sequential regression, particularly deep recurrent networks, remains a challenging and open problem. Existing approaches often mak…
Towards Automating the AI Operations Lifecycle
Matthew Arnold, Jeffrey Boston, Michael Desmond +5
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 di…