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• W2945360963 abstract "The estimation of an f-divergence between two probability distributions based on samples is a fundamental problem in statistics and machine learning. Most works study this problem under very weak assumptions, in which case it is provably hard. We consider the case of stronger structural assumptions that are commonly satisfied in modern machine learning, including representation learning and generative modelling with autoencoder architectures. Under these assumptions we propose and study an estimator that can be easily implemented, works well in high dimensions, and enjoys faster rates of convergence. We verify the behavior of our estimator empirically in both synthetic and real-data experiments, and discuss its direct implications for total correlation, entropy, and mutual information estimation." @default.
• W2945360963 created "2019-05-29" @default.
• W2945360963 creator A5010814788 @default.
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• W2945360963 date "2019-05-27" @default.
• W2945360963 modified "2023-09-27" @default.
• W2945360963 title "Practical and Consistent Estimation of f-Divergences" @default.
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• W2945360963 hasPublicationYear "2019" @default.
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