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06/27/2022
Improved Text Classification via Test-Time Augmentation
Test-time augmentation – the aggregation of predictions across transform...
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06/07/2022
Beyond Faithfulness: A Framework to Characterize and Compare Saliency Methods
Saliency methods calculate how important each input feature is to a mach...
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02/17/2021
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs
Interpretability methods aim to help users build trust in and understand...
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01/24/2021
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs
To ensure accountability and mitigate harm, it is critical that diverse ...
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11/06/2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning
ML models often exhibit unexpectedly poor behavior when they are deploye...
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05/22/2020
Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making
ML decision-aid systems are increasingly common on the web, but their su...
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11/30/2019
Image segmentation of liver stage malaria infection with spatial uncertainty sampling
Global eradication of malaria depends on the development of drugs effect...
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01/28/2019
A Framework for Understanding Unintended Consequences of Machine Learning
As machine learning increasingly affects people and society, it is impor...
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08/11/2018
Racial Disparities and Mistrust in End-of-Life Care
There are established racial disparities in healthcare, including during...
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06/30/2018
Modeling Mistrust in End-of-Life Care
In this work, we characterize the doctor-patient relationship using a ma...
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06/07/2018
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU
Machine learning approaches have been effective in predicting adverse ou...
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12/16/2015
Feature Representation for ICU Mortality
Good predictors of ICU Mortality have the potential to identify high-ris...
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01/12/2015