Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152)


One of the most common assumptions in many machine learning and data analysis tasks is that the given data points are realizations of independent and identically distributed (IID) random variables. However, this assumption is often violated, e.g., when training and test data come from different distributions (dataset bias or domain shift) or the data points… (More)
DOI: 10.4230/DagRep.5.4.18


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