70 citations · 207 across the 23 of their papers we have counts for
25 papers
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…
SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks
Davide Buffelli, Pietro Liò, Fabio Vandin
In the past few years, graph neural networks (GNNs) have become the de facto model of choice for graph classification. While, from the theoretical viewpoint, most GNNs can operate…
Graph Representation Learning for Multi-Task Settings: a Meta-Learning Approach
Davide Buffelli, Fabio Vandin
Graph Neural Networks (GNNs) have become the state-of-the-art method for many applications on graph structured data. GNNs are a model for graph representation learning, which aims…
odeN: Simultaneous Approximation of Multiple Motif Counts in Large Temporal Networks
Ilie Sarpe, Fabio Vandin
Counting the number of occurrences of small connected subgraphs, called temporal motifs, has become a fundamental primitive for the analysis of temporal networks, whose edges are a…
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…
PRESTO: Simple and Scalable Sampling Techniques for the Rigorous Approximation of Temporal Motif Counts
Ilie Sarpe, Fabio Vandin
The identification and counting of small graph patterns, called network motifs, is a fundamental primitive in the analysis of networks, with application in various domains, from so…
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…
A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings
Davide Buffelli, Fabio Vandin
Graph Neural Networks (GNNs) are a framework for graph representation learning, where a model learns to generate low dimensional node embeddings that encapsulate structural and fea…
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…
Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation
Davide Buffelli, Fabio Vandin
Sensor-based human activity recognition (HAR) requires to predict the action of a person based on sensor-generated time series data. HAR has attracted major interest in the past fe…
The Impact of Global Structural Information in Graph Neural Networks Applications
Davide Buffelli, Fabio Vandin
Graph Neural Networks (GNNs) rely on the graph structure to define an aggregation strategy where each node updates its representation by combining information from its neighbours.…
Scalable Distributed Approximation of Internal Measures for Clustering Evaluation
Federico Altieri, Andrea Pietracaprina, Geppino Pucci +1
The most widely used internal measure for clustering evaluation is the silhouette coefficient, whose naive computation requires a quadratic number of distance calculations, which i…
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…
Efficient algorithms to discover alterations with complementary functional association in cancer
Rebecca Sarto Basso, Dorit S. Hochbaum, Fabio Vandin
Recent large cancer studies have measured somatic alterations in an unprecedented number of tumours. These large datasets allow the identification of cancer-related sets of genetic…
Clustering Uncertain Graphs
Matteo Ceccarello, Carlo Fantozzi, Andrea Pietracaprina +2
An uncertain graph can be viewed as a probability space whose outcomes (referred to as \emph{possible worlds}) are subgraphs of $\ma…
Finding Mutated Subnetworks Associated with Survival in Cancer
Tommy Hansen, Fabio Vandin
Next-generation sequencing technologies allow the measurement of somatic mutations in a large number of patients from the same cancer type. One of the main goals in analyzing these…
CoMEt: A Statistical Approach to Identify Combinations of Mutually Exclusive Alterations in Cancer
Mark D. M. Leiserson, Hsin-Ta Wu, Fabio Vandin +1
Cancer is a heterogeneous disease with different combinations of genetic and epigenetic alterations driving the development of cancer in different individuals. While these alterati…
Accurate Computation of Survival Statistics in Genome-wide Studies
Fabio Vandin, Alexandra Papoutsaki, Benjamin J. Raphael +1
A key challenge in genomics is to identify genetic variants that distinguish patients with different survival time following diagnosis or treatment. While the log-rank test is wide…
Communication Lower Bounds for Distributed-Memory Computations
Michele Scquizzato, Francesco Silvestri
We give lower bounds on the communication complexity required to solve several computational problems in a distributed-memory parallel machine, namely standard matrix multiplicatio…
Space-Efficient Parallel Algorithms for Combinatorial Search Problems
Andrea Pietracaprina, Geppino Pucci, Francesco Silvestri +1
We present space-efficient parallel strategies for two fundamental combinatorial search problems, namely, backtrack search and branch-and-bound, both involving the visit of an -…
Finding the True Frequent Itemsets
Matteo Riondato, Fabio Vandin
Frequent Itemsets (FIs) mining is a fundamental primitive in data mining. It requires to identify all itemsets appearing in at least a fraction of a transactional dataset $\mat…
Mining Top-K Frequent Itemsets Through Progressive Sampling
Andrea Pietracaprina, Matteo Riondato, Eli Upfal +1
We study the use of sampling for efficiently mining the top-K frequent itemsets of cardinality at most w. To this purpose, we define an approximation to the top-K frequent itemsets…
An Efficient Rigorous Approach for Identifying Statistically Significant Frequent Itemsets
Adam Kirsch, Michael Mitzenmacher, Andrea Pietracaprina +3
As advances in technology allow for the collection, storage, and analysis of vast amounts of data, the task of screening and assessing the significance of discovered patterns is be…