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.
Amherst College Data* Mammoths
The Amherst College Data* Mammoths Learning & Research Group, led by Prof. Matteo Riondato
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Showing 10 of 11 repositories
- acdmammoths.github.io Public
Website for the Amherst College Data* Mammoths Research and Learning Group
acdmammoths/acdmammoths.github.io’s past year of commit activity - alice Public
MCMC algorithms to sample random bipartite graphs with given left and right degree sequences and BJDM.
acdmammoths/alice’s past year of commit activity - Bavarian-code Public
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
acdmammoths/Bavarian-code’s past year of commit activity - SPEck-code Public
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.
acdmammoths/SPEck-code’s past year of commit activity - parallelcubesampling Public
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).
acdmammoths/parallelcubesampling’s past year of commit activity
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