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Francesco Trovò - IEEE Xplore Author Profile

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Author details

Francesco Trovò

Publications

8

Citations

49

Publications by Year

20142024

Co-Authors:

Cesare AlippiOscar CartagenaAndrea CazzolaFilippo FedeliMieszko Ferens

Show All Co-Authors (23)

Francesco Trovò

Affiliation

Dipartimento di Elettronica Informazione e Bioingegneria

Politecnico di Milano

Milano, Italy

Publication Topics

Learning Algorithms,
Air Quality,
Bandit Feedback,
Benchmark,
Breadth-first Search,
Cancer Treatment,
Change Process,
Child Nodes,
Clinical Data,
Concept Drift,
Covariate Shift,
Coverage Levels

Biography

Francesco Trovò received the Ph.D. degree (cum laude) in information technology from the Politecnico di Milano (PoliMi), Milan, Italy, in March 2015, defending a thesis about the design of cognitive fault diagnosis systems for sensor networks. He is currently an Assistant Professor of information processing systems with the Dipartimento di Elettronica, Informazione e Bioingegneria, Artificial Intelligence and Robotics Laboratory, PoliMi. He is also the Co-Founder of ML Cube S.r.l., Milan, an innovative startup, providing cutting-edge solutions for machine learning systems and life-cycle-management optimization. He is the coauthor of more than 40 papers in peer-reviewed international conferences and journals, including Artificial Intelligenc...

Publications

8

Citations

49

Publications by Year

20142024

Co-Authors:

Cesare Alippi
Oscar Cartagena
Andrea Cazzola
Filippo Fedeli
Mieszko Ferens

Show All Co-Authors (23)

Author's Published Works

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Showing 1-8 of 8 results

Conferences (5)

Journals (3)

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Francesco Trovò(8)
Manuel Roveri(3)
Marcello Restelli(3)
Nicola Gatti(2)
Cesare Alippi(2)
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy(2)
Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy(1)
Dipartirnento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy(1)
ML Cube, Milan, Italy(1)
Instituto Sistemas Complejos de Ingeniería, Santiago, Chile(1)
IEEE Transactions on Neural Networks and Learning Systems(2)
2014 12th IEEE International Conference on Embedded and Ubiquitous Computing(1)
2014 IEEE Symposium on Intelligent Embedded Systems (IES)(1)
2017 International Joint Conference on Neural Networks (IJCNN)(1)
2018 International Joint Conference on Neural Networks (IJCNN)(1)
IEEE(8)
Media(1)
Anchorage, AK, USA(1)
Kyoto, Japan(1)
Milan, Italy(1)
Orlando, FL, USA(1)
Rio de Janeiro, Brazil(1)
Figure Of Merit(3)
Bandit Feedback(2)
Confidence Level(2)
Data Streams(2)
Detection Delay(2)

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Evolving Fuzzy Prediction Intervals in Nonstationary Environments

Oscar Cartagena;Francesco Trovò;Manuel Roveri;Doris Sáez

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Volume: 8, Issue: 1 | Journal Article |

Cited by: Papers (2)

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This work aims at designing a novel evolving fuzzy prediction interval able to model nonlinear time-variant systems. To achieve this goal, we integrate a passive mechanism meant to update the model when new data are available with an active mechanism able to trigger ad-hoc adaptation mechanisms when changes are detected in the data-generating process. The base model considered for this proposal wa...Show More

Evolving Fuzzy Prediction Intervals in Nonstationary Environments

Oscar Cartagena;Francesco Trovò;Manuel Roveri;Doris Sáez

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Volume: 8, Issue: 1 | Journal Article |

MetaLung: Towards a Secure Architecture for Lung Cancer Patient Care on the Metaverse

Michele Zanitti;Mieszko Ferens;Alberto Ferrarin;Francesco Trovò;Vanja Miskovic;Arsela Prelaj;Ming Shen;Sokol Kosta

2023 IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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The interest in metaverse applications by existing industries has seen massive growth thanks to the accelerated pace of research in key technological fields and the shift towards virtual interactions fueled by the Covid-19 pandemic. One key industry that can benefit from the integration into the metaverse is healthcare. The potential to provide enhanced care for patients affected by multiple healt...Show More

MetaLung: Towards a Secure Architecture for Lung Cancer Patient Care on the Metaverse

Michele Zanitti;Mieszko Ferens;Alberto Ferrarin;Francesco Trovò;Vanja Miskovic;Arsela Prelaj;Ming Shen;Sokol Kosta

2023 IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom)

Year: 2023 | Conference Paper |

IWDA: Importance Weighting for Drift Adaptation in Streaming Supervised Learning Problems

Filippo Fedeli;Alberto Maria Metelli;Francesco Trovò;Marcello Restelli

IEEE Transactions on Neural Networks and Learning Systems

Year: 2023 | Volume: 34, Issue: 10 | Journal Article |

Cited by: Papers (5)

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Distribution drift is an important issue for practical applications of machine learning (ML). In particular, in streaming ML, the data distribution may change over time, yielding the problem of concept drift, which affects the performance of learners trained with outdated data. In this article, we focus on supervised problems in an online nonstationary setting, introducing a novel learner-agnostic...Show More

IWDA: Importance Weighting for Drift Adaptation in Streaming Supervised Learning Problems

Filippo Fedeli;Alberto Maria Metelli;Francesco Trovò;Marcello Restelli

IEEE Transactions on Neural Networks and Learning Systems

Year: 2023 | Volume: 34, Issue: 10 | Journal Article |

Targeting Optimization for Internet Advertising by Learning from Logged Bandit Feedback

Margherita Gasparini;Alessandro Nuara;Francesco Trovò;Nicola Gatti;Marcello Restelli

2018 International Joint Conference on Neural Networks (IJCNN)

Year: 2018 | Conference Paper |

Cited by: Papers (8)

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In the last two decades, online advertising has become the most effective way to sponsor a product or an event. The success of this advertising format is mainly due to the capability of the Internet channels to reach a broad audience and to target different groups of users with specific sponsored announces. This is of paramount importance for media agencies, companies whose primary goal is to desi...Show More

Targeting Optimization for Internet Advertising by Learning from Logged Bandit Feedback

Margherita Gasparini;Alessandro Nuara;Francesco Trovò;Nicola Gatti;Marcello Restelli

2018 International Joint Conference on Neural Networks (IJCNN)

Year: 2018 | Conference Paper |

Risk-averse trees for learning from logged bandit feedback

Francesco Trovò;Stefano Paladino;Paolo Simone;Marcello Restelli;Nicola Gatti

2017 International Joint Conference on Neural Networks (IJCNN)

Year: 2017 | Conference Paper |

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Logged data is one of the most widespread form of recorded information, since it can be acquired by almost any system and stored at a little cost. Customarily, the interaction logs between the system and a user (or environment) present the structure of a sequential decision process: given a context, the system performs an action and the user provides a feedback about it. This structure is common t...Show More

Risk-averse trees for learning from logged bandit feedback

Francesco Trovò;Stefano Paladino;Paolo Simone;Marcello Restelli;Nicola Gatti

2017 International Joint Conference on Neural Networks (IJCNN)

Year: 2017 | Conference Paper |

Learning causal dependencies to detect and diagnose faults in sensor networks

Cesare Alippi;Manuel Roveri;Francesco Trovò

2014 IEEE Symposium on Intelligent Embedded Systems (IES)

Year: 2014 | Conference Paper |

Cited by: Papers (3)

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Exploiting spatial and temporal relationships in acquired datastreams is a primary ability of Cognitive Fault Detection and Diagnosis Systems (FDDSs) for sensor networks. In fact, this novel generation of FDDSs relies on the ability to correctly characterize the existing relationships among acquired datastreams to provide prompt detections of faults (while reducing false positives) and guarantee a...Show More

Learning causal dependencies to detect and diagnose faults in sensor networks

Cesare Alippi;Manuel Roveri;Francesco Trovò

2014 IEEE Symposium on Intelligent Embedded Systems (IES)

Year: 2014 | Conference Paper |

On Power and Energy Consumption Modeling for Smart Mobile Devices

Matteo Ferroni;Andrea Cazzola;Francesco Trovò;Donatella Sciuto;Marco Domenico Santambrogio

2014 12th IEEE International Conference on Embedded and Ubiquitous Computing

Year: 2014 | Conference Paper |

Cited by: Papers (5)

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In nowadays life, mobile phones are becoming a cheaper and smaller alternative to laptops for simple, everyday tasks. They experienced an astonishing growth in functionalities and, because of their constant presence in our life, mobile phones became fundamental for the interaction with information coming from the environment. Nevertheless, their resources are limited, both in terms of performance ...Show More

On Power and Energy Consumption Modeling for Smart Mobile Devices

Matteo Ferroni;Andrea Cazzola;Francesco Trovò;Donatella Sciuto;Marco Domenico Santambrogio

2014 12th IEEE International Conference on Embedded and Ubiquitous Computing

Year: 2014 | Conference Paper |

A Self-Building and Cluster-Based Cognitive Fault Diagnosis System for Sensor Networks

Cesare Alippi;Manuel Roveri;Francesco Trovò

IEEE Transactions on Neural Networks and Learning Systems

Year: 2014 | Volume: 25, Issue: 6 | Journal Article |

Cited by: Papers (25)

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Cognitive fault diagnosis systems differentiate from more traditional solutions by providing online strategies to create and update the fault-free and the faulty classes directly from incoming data. This aspect is of paramount relevance within the big data framework, since measurements are there immediately processed to detect and identify the upsurge of potential faults. This paper introduces a n...Show More

A Self-Building and Cluster-Based Cognitive Fault Diagnosis System for Sensor Networks

Cesare Alippi;Manuel Roveri;Francesco Trovò

IEEE Transactions on Neural Networks and Learning Systems

Year: 2014 | Volume: 25, Issue: 6 | Journal Article |

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