Amnon Ta-Shma
- AKSS89.M. Ajtai, J. Komlos, W. Steiger, and E. Szemeredi. Almost sorting in one round. In Advances in Computer Research, volume 5, pages 117-125, 1989.Google Scholar
- CG88.B. Chor and O. Goldreich. Unbiased bits from sources of weak randomness and probabilistic communication complexity. SIAM Journal on Computing, 17(2):230- 261, 1988. Google ScholarDigital Library
- GW94.O. Goldreich and A. Wigderson. Tiny families of functions with random properties: A quality-size trade-off for hashing. In Proceedings of the 26th Annual A CM Symposium on the Theory of Computing, ACM, pages 574-583, 1994. Google ScholarDigital Library
- NZ93.N. Nisan and D. Zuckerman. More deterministic simulation in logspace. In Proceedings of the 25th Annual A CM Symposium on the Theory o/Computing, ACM, pages 235-244, 1993. Google ScholarDigital Library
- Pip87.N. Pippenger. Sorting and selecting in rounds. SIAM Journal on Computing, 16:1032-1038, 1987. Google ScholarDigital Library
- SSZ95.M. Saks, A. Srinivasan, and S. Zhou. Explicit dispersers with polylog degree. In Proceedings o/the 26th Annual A UM Symposmm on the Theory o/Computing, ACM, 1995. Google ScholarDigital Library
- SZ94.A. Srinivasan and D. Zuckerman. Computing with very weak random sources. In Proceedings of the 35th Annual IEEE Symposium on the Foundations of Computer Science, 1994.Google ScholarDigital Library
- WZ93.A. Wigderson and D. Zuckerman. Expanders that beat the eigenvahe bound: Explicit construction and applications. In Proceedings o/the 25th Annual A CM Symposium on the Theory o/Computing, ACM, pages 245-251, 1993. Google ScholarDigital Library
- Zuc.D. Zuckerman. Randomness-optimal sampling, extractors, and constructive leader election. Private Communication.Google Scholar
- Zuc90.D. Zuckerman. General weak random sources. In Proceedings of the 31st Annual 7EEE Symposium on the Foundations o/ Computer Sczence, pages 534-543, 1990.Google ScholarDigital Library
- Zuc91.D. Zuckerman. Simulating BPP using a general weak random source. In Proceedings of the 32nd Annual IEEE Symposium on the Foundations of Computer Science, pages 79-89, 1991. Google ScholarDigital Library
- Zuc93.D. Zuckerman. NP-complete problems have a version that's hard to approximate. In Proceedings o/ the 8th Structures ~n Complexity Theory, IEEE, pages 305-312, 1993.Google ScholarCross Ref
Index Terms
- On extracting randomness from weak random sources (extended abstract)
Recommendations
Extractors with weak random seeds
STOC '05: Proceedings of the thirty-seventh annual ACM symposium on Theory of computingWe show how to extract random bits from two or more independent weak random sources in cases where only one source is of linear min-entropy and all other sources are of logarithmic min-entropy. Our main results are as follows:
- A long line of research, ...
General weak random sources
SFCS '90: Proceedings of the 31st Annual Symposium on Foundations of Computer ScienceThe following model for a weak random source is considered. The source is asked only once for R bits, and the source outputs an R-bit string such that no string has probability more than 2/sup - delta R/ of being output. for some fixed delta >0. A ...
Extracting Randomness
Extractors are Boolean functions that allow, in some precise sense, extraction of randomness from somewhat random distributions, using only a small amount of truly random bits. Extractors, and the closely related “dispersers,” exhibit some of the most “...
Comments