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The Cram Method for Efficient Simultaneous Learning and Evaluation
Our extensive simulation studies show that, when compared to sample-splitting, cramming reduces the evaluation standard error by more than 40% while improving the performance of learned policy.
Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War
First, before implementing a new algorithm, it is essential to characterize and control the risk of yielding worse outcomes than the existing algorithm.
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