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Many of the modern techniques to solve supervised learning problems suffer from a lack of interpretability and analyzability that do not give rise to rigorous mathematical results. Distributionally Robust Learning provides a detailed insight into a technique that has gained a lot of recent interest in developing robust supervised learning solutions that are founded in sound mathematical principles.
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