Crytographic limitations on learning Boolean formulae and finite automata
STOC '89 Proceedings of the twenty-first annual ACM symposium on Theory of computing
Prediction-preserving reducibility
Journal of Computer and System Sciences - 3rd Annual Conference on Structure in Complexity Theory, June 14–17, 1988
Random DFA's can be approximately learned from sparse uniform examples
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Weakly learning DNF and characterizing statistical query learning using Fourier analysis
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Efficient learning of typical finite automata from random walks
Information and Computation
General bounds on statistical query learning and PAC learning with noise via hypothesis boosting
Information and Computation
Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
Learning Random Log-Depth Decision Trees under Uniform Distribution
SIAM Journal on Computing
APPROX '08 / RANDOM '08 Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques
Exact learning of random DNF over the uniform distribution
Proceedings of the forty-first annual ACM symposium on Theory of computing
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We show that random DNF formulas, random log-depth decision trees and random deterministic finite acceptors cannot be weakly learned with a polynomial number of statistical queries with respect to an arbitrary distribution on examples.