Communications of the ACM
Theory of recursive functions and effective computability
Theory of recursive functions and effective computability
Probability and plurality for aggregations of learning machines
Information and Computation
Probabilistic inductive inference
Journal of the ACM (JACM)
The strength of noninclusions for teams of finite learners (extended abstract)
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
An introduction to computational learning theory
An introduction to computational learning theory
Breaking the probability 12 barrier in FIN-type learning
Journal of Computer and System Sciences
Finite identification of functions by teams with success ratio 1/2 and above
Information and Computation
On the structure of degrees of inferability
Journal of Computer and System Sciences
Probabilistic and team PFIN-type learning: general properties
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Computational limits on team identification of languages
Information and Computation
Probabilistic language learning under monotonicity constraints
Theoretical Computer Science - Special issue on algorithmic learning theory
FINite learning capabilities and their limits
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
The Power of Pluralism for Automatic Program Synthesis
Journal of the ACM (JACM)
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Hierarchies of probabilistic and team FIN -learning
Theoretical Computer Science
An Introduction to the General Theory of Algorithms
An Introduction to the General Theory of Algorithms
On Identification by Teams and Probabilistic Machines
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
Towards Reduction Argumentf for FINite Learning
Algorithmic Learning for Knowledge-Based Systems, GOSLER Final Report
The Power of Probabilism in Popperian FINite Learning (extended abstract)
AII '92 Proceedings of the International Workshop on Analogical and Inductive Inference
Use of Reduction Arguments in Determining Popperian FIN-Type Learning Capabilities
ALT '93 Proceedings of the 4th International Workshop on Algorithmic Learning Theory
Three Decades of Team Learning
AII '94 Proceedings of the 4th International Workshop on Analogical and Inductive Inference: Algorithmic Learning Theory
Probabilitic Limit Identification up to "Small" Sets
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
ALT '97 Proceedings of the 8th International Conference on Algorithmic Learning Theory
Hierarchies of probabilistic and team learning
Hierarchies of probabilistic and team learning
Probabilistic and team PFIN-type learning: General properties
Journal of Computer and System Sciences
Learning recursive functions: A survey
Theoretical Computer Science
Absolute versus probabilistic classification in a logical setting
Theoretical Computer Science
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Absolute versus probabilistic classification in a logical setting
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
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This paper surveys developments in probabilistic inductive inference (learning) of recursive (computable) functions. We mainly focus on finite learning, since this simple paradigm has produced the most interesting (and most complex) results.