Task-based End-to-end Model Learning in Stochastic Optimization

This paper proposes an end-to-end approach for learning probabilistic machine learning models. We show that the proposed approach can outperform both traditional modeling and purely black-box optimization approaches.

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https://arxiv.org/abs/1703.04529