Communications of the ACM
On the complexity of inductive inference
Information and Control
Probability and plurality for aggregations of learning machines
Information and Computation
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Computational limitations on learning from examples
Journal of the ACM (JACM)
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
The Power of Pluralism for Automatic Program Synthesis
Journal of the ACM (JACM)
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Graph Algorithms
Introduction To Automata Theory, Languages, And Computation
Introduction To Automata Theory, Languages, And Computation
An Introduction to the General Theory of Algorithms
An Introduction to the General Theory of Algorithms
Functions Computable in the Limit by Probabilistic Machines
Proceedings of the 3rd Symposium on Mathematical Foundations of Computer Science
Machine Inductive Inference and Language Identification
Proceedings of the 9th Colloquium on Automata, Languages and Programming
A study of grammatical inference
A study of grammatical inference
Probabilistic inductive inference
Probabilistic inductive inference
Breaking the probability ½ barrier in FIN-type learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
On learning multiple concepts in parallel
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Capabilities of probabilistic learners with bounded mind changes
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Probability is more powerful than team for language identification from positive data
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Capabilities of fallible FINite learning
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Choosing a learning team: a topological approach
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
On the impact of forgetting on learning machines
Journal of the ACM (JACM)
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Aspects of complexity of conservative probabilistic learning
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Probabilistic inductive inference: a survey
Theoretical Computer Science
Aspects of complexity of probabilistic learning under monotonicity constraints
Theoretical Computer Science - Algorithmic learning theory
Learning to win process-control games watching game-masters
Information and Computation
Avoiding coding tricks by hyperrobust learning
Theoretical Computer Science
Category, Measure, Inductive Inference: A Triality Theorem and Its Applications
LATIN '02 Proceedings of the 5th Latin American Symposium on Theoretical Informatics
Avoiding Coding Tricks by Hyperrobust Learning
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Learning to Win Process-Control Games Watching Game-Masters
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
ALT '98 Proceedings of the 9th International Conference on Algorithmic Learning Theory
PAC learning of probability distributions over a discrete domain
Theoretical Computer Science
On learning to coordinate: random bits help, insightful normal forms, and competency isomorphisms
Journal of Computer and System Sciences - Special issue: Learning theory 2003
Capabilities of Thoughtful Machines
Fundamenta Informaticae
Probabilistic and team PFIN-type learning: General properties
Journal of Computer and System Sciences
Taming teams with mind changes
Journal of Computer and System Sciences
Learning recursive functions: A survey
Theoretical Computer Science
Quantum inductive inference by finite automata
Theoretical Computer Science
Absolute versus probabilistic classification in a logical setting
Theoretical Computer Science
One-sided error probabilistic inductive inference and reliable frequency identification
Information and Computation
Learning Behaviors of Functions
Fundamenta Informaticae
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
Probabilities that imply certainties
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Absolute versus probabilistic classification in a logical setting
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Capabilities of Thoughtful Machines
Fundamenta Informaticae
Learning Via Queries With Teams And Anomalies
Fundamenta Informaticae
Learning Behaviors of Functions with Teams
Fundamenta Informaticae
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Inductive inference machines construct programs for total recursive functions given only example values of the functions. Probabilistic inductive inference machines are defined, and for various criteria of successful inference, it is asked whether a probabilistic inductive inference machine can infer larger classes of functions if the inference criterion is relaxed to allow inference with probability at least p, (0 p EX and BC), it is shown that any class of functions that can be inferred from examples with probability exceeding 1/2 can be inferred deterministically, and that for probabilities p ≤ 1/2 there is a discrete hierarchy of inferability parameterized by p. The power of probabilistic inference strategies is characterized by equating the classes of probabilistically inferable functions with those classes that can be inferred by teams of inductive inference machines (a parallel model of inference), or by a third model called frequency inference.