Learning read-once formulas with queries
Journal of the ACM (JACM)
Learning Conjunctions of Horn Clauses
Machine Learning - Computational learning theory
Cryptographic hardness of distribution-specific learning
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Constant depth circuits, Fourier transform, and learnability
Journal of the ACM (JACM)
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
When won't membership queries help?
Selected papers of the 23rd annual ACM symposium on Theory of computing
Exact learning Boolean functions via the monotone theory
Information and Computation
An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
Journal of Computer and System Sciences
Identification of gene regulatory networks by strategic gene disruptions and gene overexpressions
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Learning a circuit by injecting values
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Optimally Learning Social Networks with Activations and Suppressions
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Learning a circuit by injecting values
Journal of Computer and System Sciences
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
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We consider the problem of learning an acyclic discrete circuit with n wires, fan-in bounded by k and alphabet size s using value injection queries. For the class of transitively reduced circuits, we develop the Distinguishing Paths Algorithm, that learns such a circuit using (ns)O(k) value injection queries and time polynomial in the number of queries. We describe a generalization of the algorithm to the class of circuits with shortcut width bounded by b that uses (ns)O(k+b) value injection queries. Both algorithms use value injection queries that fix only O(kd) wires, where d is the depth of the target circuit. We give a reduction showing that without such restrictions on the topology of the circuit, the learning problem may be computationally intractable when s = nΘ(1), even for circuits of depth O(log n). We then apply our large-alphabet learning algorithms to the problem of approximate learning of analog circuits whose gate functions satisfy a Lipschitz condition. Finally, we consider models in which behavioral equivalence queries are also available, and extend and improve the learning algorithms of [5] to handle general classes of gates functions that are polynomial time learnable from counterexamples.