11 citations · 24 across the 10 of their papers we have counts for
11 papers
Few-Shot Resampling for Scalable Statistically-Sound Data Mining
Leonardo Pellegrina, Fabio Vandin
A key step in knowledge discovery is the evaluation of data mining results. In several applications, including pattern mining, graph analysis, and others, this step includes the ev…
Sampling Random Graphs from the Colored Configuration Model
Leonardo Pellegrina
A fundamental step in knowledge discovery is statistically assessing data mining results. In network analysis, such evaluation compares the outcome of a given procedure with the ou…
Fast Percolation Centrality Approximation with Importance Sampling
Antonio Cruciani, Leonardo Pellegrina
In this work we present PercIS, an algorithm based on Importance Sampling to approximate the percolation centrality of all the nodes of a graph. Percolation centrality is a general…
Scalable Rule Lists Learning with Sampling
Leonardo Pellegrina, Fabio Vandin
Learning interpretable models has become a major focus of machine learning research, given the increasing prominence of machine learning in socially important decision-making. Amon…
Efficient Discovery of Significant Patterns with Few-Shot Resampling
Leonardo Pellegrina, Fabio Vandin
Significant pattern mining is a fundamental task in mining transactional data, requiring to identify patterns significantly associated with the value of a given feature, the target…
Efficient Centrality Maximization with Rademacher Averages
Leonardo Pellegrina
The identification of the set of k most central nodes of a graph, or centrality maximization, is a key task in network analysis, with various applications ranging from finding comm…
SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds
Leonardo Pellegrina, Fabio Vandin
Betweenness centrality is a popular centrality measure with applications in several domains, and whose exact computation is impractical for modern-sized networks. We present SILVAN…
SPRISS: Approximating Frequent -mers by Sampling Reads, and Applications
Diego Santoro, Leonardo Pellegrina, Fabio Vandin
The extraction of -mers is a fundamental component in many complex analyses of large next-generation sequencing datasets, including reads classification in genomics and the char…
Sharper convergence bounds of Monte Carlo Rademacher Averages through Self-Bounding functions
Leonardo Pellegrina
We derive sharper probabilistic concentration bounds for the Monte Carlo Empirical Rademacher Averages (MCERA), which are proved through recent results on the concentration of self…
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining
Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin +1
We present MCRapper, an algorithm for efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families of functions exhibiting poset (e.g., lattice) structur…
Fast Approximation of Frequent -mers and Applications to Metagenomics
Leonardo Pellegrina, Cinzia Pizzi, Fabio Vandin
Estimating the abundances of all -mers in a set of biological sequences is a fundamental and challenging problem with many applications in biological analysis. While several met…