Machine Learning Specia Seminar

Over the last few years, kernel embedding of distributions has gained a lot of attention in the machine learning community due tothe wide variety of applications it has been employed in. Some of these applications include kernel-based non-parametric hypothesis tests, covariate-shift, density estimation, feature selection, causal inference and distribution regression. All these applications require an estimate of the kernel mean based on random samples drawn i.i.d. from an unknown distribution. Usually, an empirical estimator of the kernel mean is employed in these applications. In this talk, we propose alternative estimators of the kernel mean based on the idea of shrinkage estimation. Motivated by the classical James-Stein shrinkage for the estimation of a mean vector of a Gaussian distribution on R^d, we propose non-parametric shrinkage estimators in RKHS and establish consistency…


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