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Found 2 papers, 1 papers with code
Optimal Decision Tree with Noisy Outcomes
Our new approximation algorithms provide guarantees that are nearly best-possible and work for the general case of a large number of noisy outcomes per test or per hypothesis where the performance degrades smoothly with this number.
Adaptive Submodular Ranking and Routing
We obtain a logarithmic factor approximation algorithm for this adaptive ranking problem, which is the best possible (unless P=NP).
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