Communications of the ACM
Random generation of combinatorial structures from a uniform
Theoretical Computer Science
Computational limitations on learning from examples
Journal of the ACM (JACM)
Negative Results for Equivalence Queries
Machine Learning
Learning decision trees using the Fourier spectrum
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Learning binary relations and total orders
SIAM Journal on Computing
On the query complexity of learning
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
On the power of equivalence queries
Euro-COLT '93 Proceedings of the first European conference on Computational learning theory
Machine Learning
Machine Learning
A complexity theoretic approach to randomness
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
How many queries are needed to learn?
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Generalized teaching dimensions and the query complexity of learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
DNF—if you can't learn'em, teach'em: an interactive model of teaching
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
How many queries are needed to learn?
Journal of the ACM (JACM)
Towards the learnability of DNF formulae
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Attribute-efficient learning in query and mistake-bound models
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
A competitive approach to game learning
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Learning from examples with unspecified attribute values (extended abstract)
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
SIGACT News complexity theory column 32
ACM SIGACT News
Competing Provers Yield Improved Karp-Lipton Collapse Results
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
On Higher Arthur-Merlin Classes
COCOON '02 Proceedings of the 8th Annual International Conference on Computing and Combinatorics
Exact Learning when Irrelevant Variables Abound
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
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We show that the class of all circuits is exactly learnable in randomized expected polynomial-time using subset and superset queries. This is a consequence of the following result which we consider to be of independent interest: circuits are exactly learnable in randomized expected polynomial-time with equivalence queries and the aid of an NP-oracle. We also show that circuits are exactly learnable in deterministic polynomial-time with equivalence queries and a &Sgr;3p-oracle. The hypothesis class for the above learning algorithms is the class of circuits of larger—but polynomially related—size. Also, the algorithms can be adapted to learn the class of DNF formulas with hypothesis class consisting of depth-3 &Lgr;-V-&Lgr; formulas (by the work of Angluin, this is optimal in the sense that the hypothesis class cannot be reduced to depth-2 DNF formulas.We also investigate the power of an NP-oracle in the context of learning with membership queries. We show that there are deterministic learning algorithms that use membership queries and an NP-oracle to learn: monotone boolean functions in time polynomial in the DNF size and CNF size of the target formula; and the class of O(logn)-DNF ∩ O(logn)-CNF formulas in time polynomial in n. Finally, we show that, with an NP-oracle and membership queries, there is a randomized polynomial-time algorithm that learns any class that is learnable from membership queries with unlimited computational power.