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Given $K$ uncertainty sets that are arbitrarily dependent -- for example, confidence intervals for an unknown parameter obtained with $K$ different estimators, or prediction sets obtained via conformal prediction based on $K$ different algorithms on shared data -- we address the question of how to efficiently combine them in a black-box manner to produce a single uncertainty set.
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