International Workshop All '86 on Analogical and inductive inference
Probability and plurality for aggregations of learning machines
Information and Computation
Probabilistic inductive inference
Journal of the ACM (JACM)
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Learning recursive functions from approximations
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Robust learning aided by context
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
On the power of learning robustly
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
The Power of Pluralism for Automatic Program Synthesis
Journal of the ACM (JACM)
Robust separations in inductive inference
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
On the Uniform Learnability of Approximations to Non-Recursive Functions
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
Robust Learning - Rich and Poor
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
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The present work introduces and justifies the notion of hyperrobust learning where one fixed learner has to learn all functions in a given class plus their images under primitive recursive operators. The following is shown: This notion of learnability does not change if the class of primitive recursive operators is replaced by a larger enumerable class of operators. A class is hyperrobustly Ex-learnable iff it is a subclass of a recursively enumerable family of total functions. So, the notion of hyperrobust learning overcomes a problem of the traditional definitions of robustness which either do not preserve learning by enumeration or still permit topological coding tricks for the learning criterion Ex. Hyperrobust BC-learning as well as the hyperrobust version of Ex-learning by teams are more powerful than hyperrobust Ex-learning. The notion of bounded totally reliable BC-learning is properly between hyperrobust Ex-learning and hyperrobust BC-learning. Furthermore, the bounded totally reliably BC-learnable classes are characterized in terms of infinite branches of certain enumerable families of bounded recursive trees. A class of infinite branches of a further family of trees separates hyperrobust BC-learning from totally reliable BC-learning.