11 citations · 24 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…