When decisions must be based on incomplete (coarsened) observations and the coarsening mechanism is unknown, a minimax approach offers the best guarantees on the decision maker’s expected loss. Recent work has derived mathematical conditions characterizing minimax optimal decisions, but also found that computing such decisions is a difficult problem in general. This problem is equivalent to that of maximizing a certain conditional entropy expression. In this work, we present a highly efficient algorithm for the case where the coarsening mechanism can be represented by a tree, whose vertices are outcomes and whose edges are coarse observations.
CITATION STYLE
van Ommen, T., Koolen, W. M., & Grünwald, P. D. (2019). Efficient algorithms for minimax decisions under tree-structured incompleteness. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11726 LNAI, pp. 336–347). Springer Verlag. https://doi.org/10.1007/978-3-030-29765-7_28
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