Communications of the ACM
Negative Results for Equivalence Queries
Machine Learning
Computational learning theory: an introduction
Computational learning theory: an introduction
Constant depth circuits, Fourier transform, and learnability
Journal of the ACM (JACM)
On the computational power of depth 2 circuits with threshold and modulo gates
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain
SIAM Journal on Computing
Towards the learnability of DNF formulae
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Exact Learning of Formulas in Parallel
Machine Learning
Online Learning versus Offline Learning
Machine Learning
Learning Regular Languages from Simple Positive Examples
Machine Learning
Machine Learning
Machine Learning
Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning intersection-closed classes with signatures
Theoretical Computer Science
On exact learning halfspaces with random consistent hypothesis oracle
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
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We study a model of Probably Exactly Correct (PExact) learning that can be viewed either as the Exact model (learning from Equivalence Queries only) relaxed so that counterexamples to equivalence queries are distributionally drawn rather than adversarially chosen or as the Probably Approximately Correct (PAC) model strengthened to require a perfect hypothesis. We also introduce a model of Probably Almost Exactly Correct (PAExact) learning that requires a hypothesis with negligible error and thus lies between the PExact and PAC models. Unlike the Exact and PExact models, PAExact learning is applicable to classes of functions defined over infinite instance spaces. We obtain a number of separation results between these models. Of particular note are some positive results for efficient parallel learning in the PAExact model, which stand in stark contrast to earlier negative results for efficient parallel Exact learning.