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Algorithms and Randomness

Theory of Probability & Its Applications, 1988
This paper is one of the last works of A. N. Kolmogorov who was the founder of many branches of probability theory. (Kolmogorov died on October 20, 1987.) He was one of the initiators of the algorithmic definition of randomness. His brilliant ideas influenced very deeply this area which had also profound implications for other fields of science from ...
Kolmogorov, A. N., Uspenskij, V. A.
openaire   +3 more sources

A New Minimax Theorem for Randomized Algorithms (Extended Abstract)

IEEE Annual Symposium on Foundations of Computer Science, 2020
The celebrated minimax principle of Yao (1977) says that for any Boolean-valued function $f$ with finite domain, there is a distribution $\mu$ over the domain of $f$ such that computing $f$ to error $\epsilon$ against inputs from $\mu$ is just as hard as
S. Ben-David, Eric Blais
semanticscholar   +1 more source

Hyperparameter optimization for randomized algorithms: a case study on random features

Statistics and computing
Randomized algorithms exploit stochasticity to reduce computational complexity. One important example is random feature regression (RFR) that accelerates Gaussian process regression (GPR). RFR approximates an unknown function with a random neural network
Oliver R. A. Dunbar   +2 more
semanticscholar   +1 more source

Algorithmically random series

Computability, 2023
Rademacher (Mathematische Annalen 87 (1922) 112–138), Steinhaus (Mathematische Zeitschrift 31 (1930) 408–416) and Paley and Zygmund (Mathematical Proceedings of the Cambridge Philosophical Society 26 (1930) 337–257, Mathematical Proceedings of the Cambridge Philosophical Society 26 (1930) 458–474, Mathematical Proceedings of the Cambridge Philosophical
Downey, Rodney G.   +2 more
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Algorithmic randomness

Communications of the ACM, 2019
Tracing some of the latest advancements in algorithmic randomness.
Rod Downey, Denis R. Hirschfeldt
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Randomized Algorithms

2011
AbstractCertain situations require a random rather than a deterministic strategy. With a random strategy, the choices are unpredictable and the adversary may be kept off balance. This chapter focuses on the variety and power of randomised algorithms. More specifically, it considers algorithms that find the smallest cut in a graph by combining random ...
Cristopher Moore, Stephan Mertens
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Randomized geometric algorithms and pseudorandom generators

Algorithmica, 1992
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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