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© The Author(s) 2020.Traditionally, statistical procedures have been derived via analytic computations whoever legitimacy often relies on test size developing to infinity. We utilize resources from deep learning to develop a fresh approach, adversarial Monte Carlo meta-learning, for building optimal analytical procedures. Analytical dilemmas tend to be framed as two-player games for which Nature adversarially selects a distribution w
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