Adaptive versus nonadaptive attribute-efficient learning
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Reconstructing a Hamiltonian cycle by querying the graph: application to DNA physical mapping
Discrete Applied Mathematics - Special volume on computational molecular biology DAM-CMB series volume 2
An optimal procedure for gap closing in whole genome shotgun sequencing
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
SIAM Journal on Computing
SIAM Journal on Discrete Mathematics
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Learning a hidden graph using O( logn) queries per edge
Journal of Computer and System Sciences
Optimal query complexity bounds for finding graphs
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Learning and Verifying Graphs Using Queries with a Focus on Edge Counting
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Optimal query complexity bounds for finding graphs
Artificial Intelligence
Reconstructing weighted graphs with minimal query complexity
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Optimally reconstructing weighted graphs using queries
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Toward a deterministic polynomial time algorithm with optimal additive query complexity
MFCS'10 Proceedings of the 35th international conference on Mathematical foundations of computer science
Almost tight upper bound for finding Fourier coefficients of bounded pseudo-Boolean functions
Journal of Computer and System Sciences
Reconstruction of hidden graphs and threshold group testing
Journal of Combinatorial Optimization
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We consider the problem of learning a hypergraph using edge-detecting queries. In this model, the learner may query whether a set of vertices induces an edge of the hidden hypergraph or not. We show that an r-uniform hypergraph with m edges and n vertices is learnable with O(24rm · poly(r,logn)) queries with high probability. The queries can be made in O(min(2r (log m+r)2, (log m+r)3)) rounds. We also give an algorithm that learns an almost uniform hypergraph of dimension r using O(2O((1+Δ/2)r) · m1+Δ/2 · poly(log n)) queries with high probability, where Δ is the difference between the maximum and the minimum edge sizes. This upper bound matches our lower bound of Ω((m/(1+Δ/2))1+Δ/2) for this class of hypergraphs in terms of dependence on m. The queries can also be made in O((1+Δ) · min(2r (log m+r)2, (log m+r)3)) rounds.