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Found 4 papers, 2 papers with code
Sharp uniform convergence bounds through empirical centralization
We introduce the use of empirical centralization to derive novel practical, probabilistic, sample-dependent bounds to the Supremum Deviation (SD) of empirical means of functions in a family from their expectations.
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining
To show the practical use of MCRapper, we employ it to develop an algorithm TFP-R for the task of True Frequent Pattern (TFP) mining.
TRIÈST: Counting Local and Global Triangles in Fully-dynamic Streams with Fixed Memory Size
We present TRI\`EST, a suite of one-pass streaming algorithms to compute unbiased, low-variance, high-quality approximations of the global and local (i. e., incident to each vertex) number of triangles in a fully-dynamic graph represented as an adversarial stream of edge insertions and deletions.
Data Structures and Algorithms Databases G.2.2; H.2.8
Finding the True Frequent Itemsets
It requires to identify all itemsets appearing in at least a fraction $\theta$ of a transactional dataset $\mathcal{D}$.
