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The reliable fraction of information is an attractive score for quantifying(functional) dependencies in high-dimensional data. In this paper, wesystematically explore the algorithmic implications of using this measure foroptimization. We show that the problem is NP-hard, which justifies the usage ofworst-case exponential-time as well as heuristic search methods. We thensubstantially improve the practical performance for both optimization styles byderiving a novel admissible bounding function that has an unbounded potentialfor additional pruning over the previously proposed one. Finally, weempirically investigate the approximation ratio of the greedy algorithm andshow that it produces highly competitive results in a fraction of time neededfor complete branch-and-bound style search.

Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms