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Estimating entropy of random processes is one of the fundamental problems of machine learning and property testing. It has numerous applications to anything from DNA testing and predictability of huma...
We revisit the problem of estimating entropy of discrete distributions from independent samples, studied recently by Acharya, Orlitsky, Suresh and Tyagi (SODA 2015), improving their upper and lower bo...
We provide a new result that links two crucial entropy notions: Shannon entropy H1 and collision entropy H2. Our formula gives the \emph{worst possible} amount of collision entropy in a probability di...

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