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Maximum Mean Discrepancy Meets Neural Networks: The Radon-Kolmogorov-Smirnov Test
Maximum mean discrepancy (MMD) refers to a general class of nonparametric two-sample tests that are based on maximizing the mean difference over samples from one distribution $P$ versus another $Q$, over all choices of data transformations $f$ living in some function space $\mathcal{F}$.
Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization
Non-stationarity is ubiquitous in human behavior and addressing it in the contextual bandits is challenging.
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