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Simpler Logarithmic Approximation Algorithms for the Optimal Decision Tree and Adaptive Set Cover
We study the problem of constructing a decision tree that identifies an unknown hypothesis from a given ground set, under both average- and worst-case cost. The Optimal Decision Tree (ODT) problem has been extensively studied in the literature, and $\mathcal{O}(\log n)$-approximation guarantees are
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