Exact identification of read-once formulas using fixed points of amplification functions
SIAM Journal on Computing
Journal of Computer and System Sciences
Learning in the presence of finitely or infinitely many irrelevant attributes
Journal of Computer and System Sciences
Randomized algorithms
Optimal pooling designs with error detection
Journal of Combinatorial Theory Series A
Attribute-efficient learning in query and mistake-bound models
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Adaptive versus nonadaptive attribute-efficient learning
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Combinatorial Results on the Complexity of Teaching and Learning
MFCS '94 Proceedings of the 19th International Symposium on Mathematical Foundations of Computer Science 1994
On Learning Disjunctions of Zero-One Treshold Functions with Queries
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
Splitters and near-optimal derandomization
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Group Testing Problems with Sequences in Experimental Molecular Biology
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
On the exact learning of formulas in parallel
SFCS '92 Proceedings of the 33rd Annual Symposium on Foundations of Computer Science
PAC learning with irrelevant attributes
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Parallel Attribute-Efficient Learning of Monotone Boolean Functions
SWAT '00 Proceedings of the 7th Scandinavian Workshop on Algorithm Theory
Exact Learning when Irrelevant Variables Abound
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
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We address the problem of nonadaptive learning of Boolean functions with few relevant variables by membership queries. In another recent paper [7] we have characterized those assignment families (query sets) which are sufficient for nonadaptive learning of this function class, and we studied the query number. However, the reconstruction of the given Boolean function from the obtained responses is an important matter as well in applying such nonadaptive strategies. The computational amount for this is apparently too high if we use our query families in a straightforward way. Therefore we introduce algorithms where also the computational complexity is reasonable, rather than the query number only. The idea is to apply our assignment families to certain coarsenings of the given Boolean function, followed by simple search and verification routines.