Communications of the ACM
Matrix multiplication via arithmetic progressions
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Information Processing Letters
Learning DNF under the uniform distribution in quasi-polynomial time
COLT '90 Proceedings of the third annual workshop on Computational learning theory
SIAM Journal on Computing
Constant depth circuits, Fourier transform, and learnability
Journal of the ACM (JACM)
On learning monotone DNF formulae under uniform distributions
Information and Computation
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
An O(nlog log n) learning algorithm for DNF under the uniform distribution
Journal of Computer and System Sciences
An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
Journal of Computer and System Sciences
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Efficient noise-tolerant learning from statistical queries
Journal of the ACM (JACM)
More efficient PAC-learning of DNF with membership queries under the uniform distribution
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
On using extended statistical queries to avoid membership queries
The Journal of Machine Learning Research
Colored intersection searching via sparse rectangular matrix multiplication
Proceedings of the twenty-second annual symposium on Computational geometry
Quantum Algorithms for Learning and Testing Juntas
Quantum Information Processing
DNF are teachable in the average case
Machine Learning
Algorithms for Inference, Analysis and Control of Boolean Networks
AB '08 Proceedings of the 3rd international conference on Algebraic Biology
Improved Bounds for Testing Juntas
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
Property Testing: A Learning Theory Perspective
Foundations and Trends® in Machine Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Application of a generalization of russo's formula to learning from multiple random oracles
Combinatorics, Probability and Computing
Learning and lower bounds for AC0 with threshold gates
APPROX/RANDOM'10 Proceedings of the 13th international conference on Approximation, and 14 the International conference on Randomization, and combinatorial optimization: algorithms and techniques
Testing juntas: a brief survey
Property testing
Testing juntas: a brief survey
Property testing
A note on quantum algorithms and the minimal degree of ε-error polynomials for symmetric functions
Quantum Information & Computation
On noise-tolerant learning of sparse parities and related problems
ALT'11 Proceedings of the 22nd international conference on Algorithmic learning theory
On exact learning from random walk
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
On the degree of univariate polynomials over the integers
Proceedings of the 3rd Innovations in Theoretical Computer Science Conference
DNF are teachable in the average case
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Learning juntas in the presence of noise
TAMC'06 Proceedings of the Third international conference on Theory and Applications of Models of Computation
When does greedy learning of relevant attributes succeed?: a fourier-based characterization
COCOON'07 Proceedings of the 13th annual international conference on Computing and Combinatorics
Learnability of DNF with representation-specific queries
Proceedings of the 4th conference on Innovations in Theoretical Computer Science
Properties and applications of boolean function composition
Proceedings of the 4th conference on Innovations in Theoretical Computer Science
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We consider a fundamental problem in computational learning theory: learning an arbitrary Boolean function that depends on an unknown set of k out of n Boolean variables. We give an algorithm for learning such functions from uniform random examples that runs in time roughly (nk)ω/ω+1, where ω nk time bound which can be achieved via exhaustive search. Our algorithm and analysis exploit new structural properties of Boolean functions.