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
Rounds in communication complexity revisited
STOC '91 Proceedings of the twenty-third 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
Making polynomials robust to noise
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
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Suppose we want to compute a Boolean function f, but instead of receiving the input, we only get l ε-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 show that if f : {0,1}n → {0, 1} and ε are known, the best function construction, F, is often not the trivial one. In particular, in many cases the best F cannot be written as a composition of f with some functions, 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)=Θ(l(f)). Moreover, both quantities are in Θ(log S(f)), where S(f) is the sensitivity of f.A quantity related to l(f) is Dstat.εrand(f) = min Σi = 1n li where li is the number of 1/10-biased copies of xi such that the above number of readings is sufficient to recover f with high probability. This quantity was first introduced by Reischuk and Schmeltz [14] in order to provide lower bounds for the noisy circuit size of f. In this article we give a complete characterization of Dstat.εrand(f) through a combinatorial lemma that can be interesting on its own right.