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04/21/2023
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
Data collected from the real world tends to be biased, unbalanced, and a...
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08/23/2022
Anomaly Attribution with Likelihood Compensation
This paper addresses the task of explaining anomalous predictions of a b...
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06/02/2021
Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI
In this paper, we describe an open source Python toolkit named Uncertain...
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06/01/2021
Uncertainty Characteristics Curves: A Systematic Assessment of Prediction Intervals
Accurate quantification of model uncertainty has long been recognized as...
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12/15/2020
Learning Prediction Intervals for Model Performance
Understanding model performance on unlabeled data is a fundamental chall...
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07/10/2020
Not Your Grandfathers Test Set: Reducing Labeling Effort for Testing
Building and maintaining high-quality test sets remains a laborious and ...
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07/02/2020
Uncertainty Prediction for Deep Sequential Regression Using Meta Models
Generating high quality uncertainty estimates for sequential regression,...
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03/28/2020
Towards Automating the AI Operations Lifecycle
Today's AI deployments often require significant human involvement and s...
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05/14/2018
Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes
We propose a confidence scoring mechanism for multi-layer neural network...
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08/14/2017
