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Found 2 papers, 1 papers with code
A Rademacher Complexity Based Method fo rControlling Power and Confidence Level in Adaptive Statistical Analysis
While standard statistical inference techniques and machine learning generalization bounds assume that tests are run on data selected independently of the hypotheses, practical data analysis and machine learning are usually iterative and adaptive processes where the same holdout data is often used for testing a sequence of hypotheses (or models), which may each depend on the outcome of the previous tests on the same data.
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
