8 items found

Tags: Machine Learning

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  • JournalArticle

    A Critical Reassessment of the Saerens-Latinne-Decaestecker Algorithm

    We critically re-examine the Saerens-Latinne-Decaestecker (SLD) algorithm, a well-known method for estimating class prior probabilities (“priors”) and adjusting posterior...
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  • ConferencePaper

    Boilerplate Removal using a Neural Sequence Labeling Model

    The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web...
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  • ConferencePaper

    Private traits and attributes are predictable from digital records of human b...

    We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes...
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  • ConferencePaper

    Beyond Distributive Fairness in Algorithmic Decision Making

    Beyond Distributive Fairness in Algorithmic Decision Making Feature Selection for Procedurally Fair Learning With widespread use of machine learning methods in numerous...
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  • ConferencePaper

    A comparative study of fairness enhancing interventions in machine learning

    Computers are increasingly used to make decisions that have significant impact on people's lives. Often, these predictions can affect different population subgroups...
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  • JournalArticle

    Will big data algorithms dismantle the foundations of liberalism

    In Homo Deus, Yuval Noah Harari argues that technological advances of the twenty-first century will usher in a significant shift in how humans make important life decisions....
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  • JournalArticle

    Fair Prediction with Disparate Impact A Study of Bias in Recidivism Predictio...

    Recidivism prediction instruments (RPIs) provide decision-makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time....
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  • ConferencePaper

    Why Are Learned Indexes So Effective

    A recent trend in algorithm design consists of augmenting classic data structures with machine learning models, which are better suited to reveal and exploit patterns and trends...
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