Corrupted data






Identification and Robustness with Contaminated and Corrupted Data

AND CORRUPTED DATA. BY JOEL L. HOROWITZ AND CHARLES F. MANSKI. Robust estimation aims at developing point estimators that are not highly sensitive to.


A Theory of Learning with Corrupted Labels

We demonstrate a generic method for learning from corrupted data if the corruption is reconstructible. We show the utility of this framework via concrete novel 


Corrupted Data and the Illicit Arms Trade

6 abr. 2020 amount of corrupt data present impacts posterior statistics such as the ... underreporting their arms trading resulting in corrupted data.
Graves Rose ?sequence= &isAllowed=y


MentorNet: Learning Data-Driven Curriculum for Very Deep Neural

To overcome the overfitting on corrupted labels we propose a novel technique of learning another neural network
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NLI Data Sanity Check: Assessing the Effect of Data Corruption on

If model accuracy on the corrupted data remains high then the dataset is likely to contain statistical biases and artefacts that guide prediction.
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An Analysis of Data Corruption in the Storage Stack

disk error – silent data corruption where the data is silently corrupted with no indication from the drive that an error has occurred.
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Robust Risk Minimization for Statistical Learning From Corrupted Data

INDEX TERMS Data corruption Huber contamination model
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Fairness-Aware PAC Learning from Corrupted Data

Fairness and data corruption. Most relevant for our setup are a number of recent works that empirically study attacks and defenses on fair learners under 





Using Trusted Data to Train Deep Networks on Labels Corrupted by

Sources of label noise include automatic labeling non-expert labeling


Regression Robustness under Training-Time Random Data

the training-size is reduced in order to remove the corrupted data. While the neural network appears to be the most robust method among these three 
SA FlansburgC


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