<ul><li>The paper discusses the impacts of censored feedback on generalization error bounds in learning algorithms.</li><li>Censored feedback refers to situations where the true label of a data point is only revealed if a favorable decision is made.</li><li>The paper presents an extension of the Dvoretzky-Kiefer-Wolfowitz inequality to quantify the gap between empirical and theoretical data distribution CDFs in non-IID data due to censored feedback.</li><li>The analysis highlights the need for new error bounds that account for censored feedback to accurately capture a model's generalization guarantees.</li></ul>

Generalization Error Bounds for Learning under Censored Feedback

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