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Application of entropic value-at-risk in machine learning with corrupted input data

The entropic value-at-risk (EVaR) is a coherent risk measure that is efficiently computable for the sum of independent random variables. This paper shows how this risk measure can be used in machine learning when uncertainty affects the input data. For this purpose, we consider here a support vector machine with corrupted input data.