Leonardo Pellegrina | Publications

Fast Percolation Centrality Approximation with Importance Sampling

Antonio Cruciani, Leonardo Pellegrina. Proceedings of the IEEE International Conference on Data Mining (ICDM 2025).

DOI arXiv Code

Scalable Rule Lists Learning with Sampling

Leonardo Pellegrina, Fabio Vandin. Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2024).

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Efficient Discovery of Significant Patterns with Few-Shot Resampling

Leonardo Pellegrina, Fabio Vandin. Proceedings of the VLDB Endowment 17 (10), (2024).

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SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds

Leonardo Pellegrina, Fabio Vandin. ACM Transactions on Knowledge Discovery and Data Mining (2023).

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Efficient Centrality Maximization with Rademacher Averages

Leonardo Pellegrina. Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2023).

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Discovering Significant Evolutionary Trajectories in Cancer Phylogenies

Leonardo Pellegrina, Fabio Vandin. Bioinformatics (Proceedings of ECCB 2022).

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SPRISS: Approximating Frequent k-mers by Sampling Reads, and Applications

Diego Santoro, Leonardo Pellegrina, Matteo Comin, Fabio Vandin. Bioinformatics (2022).

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MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining

Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato. ACM Transactions on Knowledge Discovery and Data Mining (2022).

DOI Code

SPRISS: Approximating Frequent k-mers by Sampling Reads, and Applications

Diego Santoro, Leonardo Pellegrina, Fabio Vandin. Proceedings of the 25th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2021).

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Rigorous and Efficient Algorithms for Significant and Approximate Pattern Mining

Leonardo Pellegrina. Ph.D. thesis, 2021.

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MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining

Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2020).

DOI arXiv Code

Efficient Mining of the Most Significant Patterns with Permutation Testing

Leonardo Pellegrina, Fabio Vandin. Data Mining & Knowledge Discovery 34 (2020)..

DOI Code

Fast Approximation of Frequent k-mers and Applications to Metagenomics

Leonardo Pellegrina, Cinzia Pizzi, Fabio Vandin. Journal of Computational Biology Special Issue of Best Papers from the RECOMB 2019 conference, 27, 4, (2020).

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SPuManTE: Significant Pattern Mining with Unconditional Testing

Leonardo Pellegrina, Matteo Riondato, Fabio Vandin. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2019).

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Multiple Hypothesis Testing and Statistically-sound Pattern Mining

Leonardo Pellegrina, Matteo Riondato, Fabio Vandin. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2019).

DOI

Fast Approximation of Frequent k-mers and Applications to Metagenomics

Leonardo Pellegrina, Cinzia Pizzi, Fabio Vandin. Proceedings of the 23th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2019).

DOI arXiv Code

Efficient Mining of the Most Significant Patterns with Permutation Testing

Leonardo Pellegrina, Fabio Vandin. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2018).

DOI Code