Communications of the ACM
Computational learning theory: an introduction
Computational learning theory: an introduction
Toward efficient agnostic learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Learning in the presence of malicious errors
SIAM Journal on Computing
Decision theoretic generalizations of the PAC model for neural net and other learning applications
Information and Computation
Robust trainability of single neurons
Journal of Computer and System Sciences
The complexity and approximability of finding maximum feasible subsystems of linear relations
Theoretical Computer Science
Some optimal inapproximability results
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Machine Learning
On the Difficulty of Approximately Maximizing Agreements
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Bounds for the Minimum Disagreement Problem with Applications to Learning Theory
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Bounds for the Minimum Disagreement Problem with Applications to Learning Theory
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
Some connections between learning and optimization
Discrete Applied Mathematics - Discrete mathematics & data mining (DM & DM)
Some connections between learning and optimization
Discrete Applied Mathematics
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This paper studies 驴-CoAgnostic learnability of classes of boolean formulas. To 驴-CoAgnostic learn C from H, the learner seeks a hypothesis h 驴 H that agrees (rather than disagrees as in Agnostic learning) within a factor 驴 of the best agreement of any f 驴 C. Although 1-CoAgnostic learning is equivalent to Agnostic learning, this is not true for 驴-CoAgnostic learning for 1/2