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Our method, called deep\ninverse optimization, is to unroll an iterative optimization process and then\nuse backpropagation to learn parameters that generate the observations. We\ndemonstrate that by backpropagating through the interior point algorithm we can\nlearn the coefficients determining the cost vector and the constraints,\nindependently or jointly, for both non-parametric and parametric linear\nprograms, starting from one or multiple observations. 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