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Naive Feature Selection: Sparsity in Naive Bayes

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IDSS Distinguished Seminars Michael Kearns (University of Pennsylvania)

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Naive Feature Selection: Sparsity in Naive Bayes

Alexandre d'Aspremont (ENS, CNRS)
online

Abstract: Due to its linear complexity, naive Bayes classification remains an attractive supervised learning method, especially in very large-scale settings. We propose a sparse version of naive Bayes, which can be used for feature selection. This leads to a combinatorial maximum-likelihood problem, for which we provide an exact solution in the case of binary data,…

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The Ethical Algorithm

Michael Kearns (University of Pennsylvania)
online

Title: The Ethical Algorithm Abstract: Many recent mainstream media articles and popular books have raised alarms over anti-social algorithmic behavior, especially regarding machine learning and artificial intelligence. The concerns include leaks of sensitive personal data by predictive models, algorithmic discrimination as a side-effect of machine learning, and inscrutable decisions made by complex models. While standard…

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