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• W2971567195 abstract "A crucial assumption in most statistical learning theory is that samples are independently and identically distributed (i.i.d.). However, for many real applications, the i.i.d. assumption does not hold. We consider learning problems in which examples are dependent and their dependency relation is characterized by a graph. To establish algorithm-dependent generalization theory for learning with non-i.i.d. data, we first prove novel McDiarmid-type concentration inequalities for Lipschitz functions of graph-dependent random variables. We show that concentration relies on the forest complexity of the graph, which characterizes the strength of the dependency. We demonstrate that for many types of dependent data, the forest complexity is small and thus implies good concentration. Based on our new inequalities we are able to build stability bounds for learning from graph-dependent data." @default.
• W2971567195 created "2019-09-12" @default.
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• W2971567195 date "2019-09-05" @default.
• W2971567195 modified "2023-09-23" @default.
• W2971567195 title "McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds" @default.
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• W2971567195 hasPublicationYear "2019" @default.
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