On Using Extended Statistical Queries to Avoid Membership Queries
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
On using extended statistical queries to avoid membership queries
The Journal of Machine Learning Research
Evolvability from learning algorithms
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Separating Models of Learning with Faulty Teachers
ALT '07 Proceedings of the 18th international conference on Algorithmic Learning Theory
Separating models of learning with faulty teachers
Theoretical Computer Science
On attribute efficient and non-adaptive learning of parities and DNF expressions
COLT'05 Proceedings of the 18th annual conference on Learning Theory
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Kearns introduced the "statistical query" (SQ) model as a general method for producing learning algorithms which are robust against classification noise. We extend this approach in several ways, in order to tackle algorithms that use ``membership queries", focusing on the more stringent model of "persistent noise". The main ingredients in the general analysis are: 1. Smallness of dimension of both the targets' class and the queries' class. 2. Independence of the noise variables. Persistence restricts independence, forcing repeated invocation of the same point x to give the same label. We apply the general analysis and ad-hoc considerations to get a noise-robust version of Jackson's Harmonic Sieve, which learns DNF under the uniform distribution. This corrects an error in his earlier analysis of noise tolerant DNF learning.