Almost everywhere high nonuniform complexity
Journal of Computer and System Sciences
Measure, Stochasticity, and the Density of Hard Languages
SIAM Journal on Computing
Reductions to sets of low information content
Complexity theory
How fast can a threshold gate learn?
Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
With Quasilinear Queries EXP is not Polynomial Time Turing Reducible to Sparse Sets
SIAM Journal on Computing
Geometric sets of low information content
Theoretical Computer Science
The quantitative structure of exponential time
Complexity theory retrospective II
Twelve problems in resource-bounded measure
Current trends in theoretical computer science
The Density of Weakly Complete Problems under Adaptive Reductions
SIAM Journal on Computing
Dimension in Complexity Classes
SIAM Journal on Computing
Online Learning and Resource-Bounded Dimension: Winnow Yields New Lower Bounds for Hard Sets
SIAM Journal on Computing
Effective Strong Dimension in Algorithmic Information and Computational Complexity
SIAM Journal on Computing
A new algorithm for minimizing convex functions over convex sets
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
Weights of exact threshold functions
MFCS'10 Proceedings of the 35th international conference on Mathematical foundations of computer science
Axiomatizing resource bounds for measure
CiE'11 Proceedings of the 7th conference on Models of computation in context: computability in Europe
NE is not NP turing reducible to nonexponentially dense NP sets
LATIN'12 Proceedings of the 10th Latin American international conference on Theoretical Informatics
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We use the connection between resource-bounded dimension and the online mistake-bound model of learning to show that the following classes have polynomial-time dimension zero. 1. The class of problems which reduce to nondense sets via a majority reduction.