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This paper proposes an end-to-end\napproach for learning probabilistic machine learning models in a manner that\ndirectly captures the ultimate task-based objective for which they will be\nused, within the context of stochastic programming. We present three\nexperimental evaluations of the proposed approach: a classical inventory stock\nproblem, a real-world electrical grid scheduling task, and a real-world energy\nstorage arbitrage task. 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