Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Active Learning Using Arbitrary Binary Valued Queries
Machine Learning
Journal of Computer and System Sciences
Generalized teaching dimensions and the query complexity of learning
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Sampling lower bounds via information theory
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Theoretical Computer Science - Special issue: Algorithmic learning theory
ICML '06 Proceedings of the 23rd international conference on Machine learning
Active learning in the non-realizable case
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Average-case active learning with costs
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Bayesian active learning using arbitrary binary valued queries
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Models of Cooperative Teaching and Learning
The Journal of Machine Learning Research
Active learning via perfect selective classification
The Journal of Machine Learning Research
Activized learning: transforming passive to active with improved label complexity
The Journal of Machine Learning Research
A theory of transfer learning with applications to active learning
Machine Learning
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We study the label complexity of pool-based active learning in the PAC model with noise. Taking inspiration from extant literature on Exact learning with membership queries, we derive upper and lower bounds on the label complexity in terms of generalizations of extended teaching dimension. Among the contributions of this work is the first nontrivial general upper bound on label complexity in the presence of persistent classification noise.