Consistent Vector-valued Distribution Regression
نویسنده
چکیده
We address the distribution regression problem (DRP): regressing on the (possibly infinite dimensional) domain of probability measures, in the two-stage sampled setup when only samples from the distributions are observable. The DRP formulation offers a unified framework for several important tasks in statistics and machine learning including for example, drug activity prediction, aerosol optical depth estimation, or supervised entropy learning.
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