Quantum Mean Embedding of Probability Distributions

31 May 2019Jonas M. KüblerKrikamol MuandetBernhard Schölkopf

The kernel mean embedding of probability distributions is commonly used in machine learning as an injective mapping from distributions to functions in an infinite dimensional Hilbert space. It allows us, for example, to define a distance measure between probability distributions, called maximum mean discrepancy (MMD)... (read more)

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