Search Results for author:
Found 3 papers, 1 papers with code
Compression of enumerations and gain
We study the compressibility of enumerations, and its role in the relative Kolmogorov complexity of computably enumerable sets, with respect to density.
The idemetric property: when most distances are (almost) the same
We introduce the \emph{idemetric} property, which formalises the idea that most nodes in a graph have similar distances between them, and which turns out to be quite standard amongst small-world network models.
Social and Information Networks Discrete Mathematics
Algorithmic learning of probability distributions from random data in the limit
We study the problem of identifying a probability distribution for some given randomly sampled data in the limit, in the context of algorithmic learning theory as proposed recently by Vinanyi and Chater.
