A learning and research group at Amherst College, led by Matteo Riondato, working on everything data-related.
Please see our home page for an introduction to the group.
A learning and research group at Amherst College, led by Matteo Riondato, working on everything data-related.
Please see our home page for an introduction to the group.
Website for the Amherst College Data* Mammoths Research and Learning Group
Code for the paper "Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages", by Chloe Wohlgemuth, Cyrus Cousins, and Matteo Riondato, appearing in ACM KDD'21 and ACM TKDD'23
Code for the paper "SPEck: Mining Statistically-significant Sequential Patterns Efficiently with Exact Sampling", by Steedman Jenkins, Stefan Walzer-Goldfeld, and Matteo Riondato, appearing in the Data Mining and Knowledge Discovery Special Issue for ECML PKDD'22.
Implementations of the parallel and sequential cube sampling algorithms presented in the paper "A Scalable Parallel Algorithm for Balanced Sampling" (Alexander Lee, Stefan Walzer-Goldfeld, Shukry Zablah, Matteo Riondato, AAAI'22 Student Abstract).