Asking the right questions: model-driven optimization using probes
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Model-driven optimization using adaptive probes
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Computing mean absolute deviation under uncertainty
Applied Soft Computing
How to probe for an extreme value
ACM Transactions on Algorithms (TALG)
Adaptive Uncertainty Resolution in Bayesian Combinatorial Optimization Problems
ACM Transactions on Algorithms (TALG)
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We consider a new model for computing with uncertainty. It is desired to compute a function f(X1,. . .,Xn), where X1, . . ., Xn are unknown but guaranteed to lie in specified intervals I1, . . ., In. It is possible to query the precise value of any Xj at a cost cj. The goal is to pin down the value of f to within a precision $\delta$ at a minimum possible cost. We focus on the selection function f which returns the value of the kth smallest argument. We present optimal offline and online algorithms for this problem.