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We propose a new framework for reasoning about information in complex systems.
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The variational fair autoencoder
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Estimating mutual information for discrete-continuous mixtures
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Mutual information neural estimation
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Multiclass classification, information, divergence and surrogate risk
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Learning adversarially fair and transferable representations
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Learning controllable fair representations
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On variational bounds of mutual information
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Understanding the limitations of variational mutual information estimators
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