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TRIÈST: Counting Local and Global Triangles in Fully-Dynamic Streams with Fixed Memory Size.

, , , and . KDD, page 825-834. ACM, (2016)

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Wiggins: Detecting Valuable Information in Dynamic Networks Using Limited Resources., , and . WSDM, page 677-686. ACM, (2016)ProSecCo: Progressive Sequence Mining with Convergence Guarantees., , and . ICDM, page 417-426. IEEE Computer Society, (2018)Finding the True Frequent Itemsets., and . SDM, page 497-505. SIAM, (2014)ProSecCo: progressive sequence mining with convergence guarantees., , and . Knowl. Inf. Syst., 62 (4): 1313-1340 (2020)MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining., , , and . ACM Trans. Knowl. Discov. Data, 16 (6): 124:1-124:29 (2022)Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages., , and . KDD, page 196-206. ACM, (2021)CaDET: interpretable parametric conditional density estimation with decision trees and forests., and . Mach. Learn., 108 (8-9): 1613-1634 (2019)ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages., and . KDD, page 1145-1154. ACM, (2016)The VC-Dimension of SQL Queries and Selectivity Estimation through Sampling., , , , and . ECML/PKDD (2), volume 6912 of Lecture Notes in Computer Science, page 661-676. Springer, (2011)MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining., , , and . KDD, page 2165-2174. ACM, (2020)