Zobrazeno 1 - 10
of 614
pro vyhledávání: '"Vandin, A"'
Autor:
Leguizamon-Robayo, Alexander, Jiménez-Pastor, Antonio, Tribastone, Micro, Tschaikowski, Max, Vandin, Andrea
Gaining insights from realistic dynamical models of biochemical systems can be challenging given their large number of state variables. Model reduction techniques can mitigate this by decreasing complexity by mapping the model onto a lower-dimensiona
Externí odkaz:
http://arxiv.org/abs/2411.14242
Autor:
Boldrin, Cristian, Vandin, Fabio
In this work, we present the first efficient and practical algorithm for estimating the number of triangles in a graph stream using predictions. Our algorithm combines waiting room sampling and reservoir sampling with a predictor for the heaviness of
Externí odkaz:
http://arxiv.org/abs/2409.15205
Structural network embedding is a crucial step in enabling effective downstream tasks for complex systems that aims to project a network into a lower-dimensional space while preserving similarities among nodes. We introduce a simple and efficient emb
Externí odkaz:
http://arxiv.org/abs/2409.10160
Autor:
Pellegrina, Leonardo, Vandin, Fabio
Learning interpretable models has become a major focus of machine learning research, given the increasing prominence of machine learning in socially important decision-making. Among interpretable models, rule lists are among the best-known and easily
Externí odkaz:
http://arxiv.org/abs/2406.12803
Autor:
Pellegrina, Leonardo, Vandin, Fabio
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. In several applications, such as biomedicine, basket market analysis
Externí odkaz:
http://arxiv.org/abs/2406.11803
Finding dense subnetworks, with density based on edges or more complex structures, such as subgraphs or $k$-cliques, is a fundamental algorithmic problem with many applications. While the problem has been studied extensively in static networks, much
Externí odkaz:
http://arxiv.org/abs/2406.10608
We present a novel, simple and widely applicable semi-supervised procedure for anomaly detection in industrial and IoT environments, SAnD (Simple Anomaly Detection). SAnD comprises 5 steps, each leveraging well-known statistical tools, namely; smooth
Externí odkaz:
http://arxiv.org/abs/2404.17925
Autor:
Casaluce, Roberto, Burattin, Andrea, Chiaromonte, Francesca, Lafuente, Alberto Lluch, Vandin, Andrea
We propose a novel methodology for validating software product line (PL) models by integrating Statistical Model Checking (SMC) with Process Mining (PM). Our approach focuses on the feature-oriented language QFLan in the PL engineering domain, allowi
Externí odkaz:
http://arxiv.org/abs/2401.13019
Autor:
Jiménez-Pastor, Antonio, Toller, Daniele, Tribastone, Mirco, Tschaikowski, Max, Vandin, Andrea
Positive systems naturally arise in situations where the model tracks physical quantities. Although the linear case is well understood, analysis and controller design for nonlinear positive systems remain challenging. Model reduction methods can help
Externí odkaz:
http://arxiv.org/abs/2312.08831
The ability to control complex networks is of crucial importance across a wide range of applications in natural and engineering sciences. However, issues of both theoretical and numerical nature introduce fundamental limitations to controlling large-
Externí odkaz:
http://arxiv.org/abs/2312.07421