Unbiased bits from sources of weak randomness and probabilistic communication complexity
SIAM Journal on Computing - Special issue on cryptography
Learning simple concepts under simple distributions
SIAM Journal on Computing
Average case complexity under the universal distribution equals worst-case complexity
Information Processing Letters
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Extracting randomness: a survey and new constructions
Journal of Computer and System Sciences
Extracting Randomness Using Few Independent Sources
SIAM Journal on Computing
Randomness extractors for independent sources and applications
Randomness extractors for independent sources and applications
A 2-Source Almost-Extractor for Linear Entropy
APPROX '08 / RANDOM '08 Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques
Unbalanced expanders and randomness extractors from Parvaresh--Vardy codes
Journal of the ACM (JACM)
2-Source Extractors under Computational Assumptions and Cryptography with Defective Randomness
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
Two Sources Are Better than One for Increasing the Kolmogorov Complexity of Infinite Sequences
Theory of Computing Systems - Special Issue: Symposium on Computer Science; Guest Editors: Sergei Artemov, Volker Diekert and Alexander Razborov
Extracting Kolmogorov complexity with applications to dimension zero-one laws
Information and Computation
Increasing kolmogorov complexity
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
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We clarify the role of Kolmogorov complexity in the area of randomness extraction. We show that a computable function is an almost randomness extractor if and only if it is a Kolmogorov complexity extractor, thus establishing a fundamental equivalence between two forms of extraction studied in the literature: Kolmogorov extraction and randomness extraction. We present a distribution Mk based on Kolmogorov complexity that is complete for randomness extraction in the sense that a computable function is an almost randomness extractor if and only if it extracts randomness from Mk.