Invariance of complexity measures for networks with unreliable gates
Journal of the ACM (JACM)
Computing with unreliable information
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
SIAM Journal on Computing
Lower bounds for the complexity of reliable boolean circuits with noisy gates
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Reliable computation with noisy circuits and decision trees—a general n log n lower bound
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
Sensitivity vs. block sensitivity (an average-case study)
Information Processing Letters
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
The influence of variables on Boolean functions
SFCS '88 Proceedings of the 29th Annual Symposium on Foundations of Computer Science
Rounds vs queries trade-off in noisy computation
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Lower Bounds for the Noisy Broadcast Problem
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Robust polynomials and quantum algorithms
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
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Suppose, we want to compute a Boolean function f, but instead of receiving the input, we only get l 驴-faulty copies of each input bit. A typical solution in this case is to take the majority value of the faulty bits for each individual input bit and apply f on the majority values. We call this the trivial construction.We showt hat if f : {0, 1}n 驴 {0, 1} and 驴 are known, the best construction function, F, is often not the trivial. In particular, in many cases the best F cannot be written as a composition of some functions with f, and in addition it is better to use a randomized F than a deterministic one. We also prove, that the trivial construction is optimal in some rough sense: if we denote by l(f) the number of 1/10 -biased copies we need from each input to reliably compute f using the best (randomized) recovery function F, and we denote by ltriv(f) the analogous number for the trivial construction, then ltriv(f) = 驴(l(f)). Moreover, both quantities are in 驴(log S(f)), where S(f) is the sensitivity of f.A quantity related to l(f) is Dstat,驴rand(f) = min驴i=1n li, where li is the number of 0.1-biased copies of xi, such that the above number of readings is already sufficient to recover f with high certainty. This quantity was first introduced by Reischuk et al. [14] in order to provide lower bounds for the noisy circuit size of f. In this article we give a complete characterization of Dstat,驴rand(f) through a combinatorial lemma, that can be interesting on its own right.