Mirko Polato

According to our database, Mirko Polato authored at least 45 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A Survey on Hypergraph Representation Learning.

Alessia Antelmi, Gennaro Cordasco, Mirko Polato, Vittorio Scarano, Carmine Spagnuolo, Dingqi Yang

ACM Comput. Surv., January, 2024

2023

Experimenting with Emerging ARM and RISC-V Systems for Decentralised Machine Learning.

Gianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli, Doriana Medic, Andrea Bartolini, Roberto Esposito, Emanuele Parisi, Francesco Beneventi, Mirko Polato, Massimo Torquati, Luca Benini, Marco Aldinucci

CoRR, 2023

1st Workshop on Federated Learning Technologies.

Mirko Polato, Roberto Esposito, Walter Riviera, Zenglin Xu, Irwin King

Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 2023

Boosting Methods for Federated Learning.

Roberto Esposito, Mirko Polato, Marco Aldinucci

Proceedings of the 31st Symposium of Advanced Database Systems, 2023

Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning.

Gianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli, Doriana Medic, Andrea Bartolini, Roberto Esposito, Emanuele Parisi, Francesco Beneventi, Mirko Polato, Massimo Torquati, Luca Benini, Marco Aldinucci

Proceedings of the 20th ACM International Conference on Computing Frontiers, 2023

2022

Dissociation Between Users' Explicit and Implicit Attitudes Toward Artificial Intelligence: An Experimental Study.

Valentina Fietta, Francesca Zecchinato, Brigida Di Stasi, Mirko Polato, Merylin Monaro

IEEE Trans. Hum. Mach. Syst., 2022

On the feasibility of crawling-based attacks against recommender systems.

Fabio Aiolli, Mauro Conti, Stjepan Picek, Mirko Polato

J. Comput. Secur., 2022

PRL: A game theoretic large margin method for interpretable feature learning.

Mirko Polato, Guglielmo Faggioli, Fabio Aiolli

Neurocomputing, 2022

Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent.

Mirko Polato, Roberto Esposito, Marco Aldinucci

Proceedings of the International Joint Conference on Neural Networks, 2022

Novel Applications for VAE-based Anomaly Detection Systems.

Luca Bergamin, Tommaso Carraro, Mirko Polato, Fabio Aiolli

Proceedings of the International Joint Conference on Neural Networks, 2022

Conditioned Variational Autoencoder for Top-N Item Recommendation.

Tommaso Carraro, Mirko Polato, Luca Bergamin, Fabio Aiolli

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2022, 2022

Bayes Point Rule Set Learning.

Mirko Polato, Fabio Aiolli, Luca Bergamin, Tommaso Carraro

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

2021

Propositional Kernels.

Mirko Polato, Fabio Aiolli

Entropy, 2021

Efficient Multilingual Deep Learning Model for Keyword Categorization.

Mirko Polato, Denys Demchenko, Almat Kuanyshkereyev, Nicolò Navarin

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

Federated Variational Autoencoder for Collaborative Filtering.

Mirko Polato

Proceedings of the International Joint Conference on Neural Networks, 2021

Privacy-Preserving Kernel Computation For Vertically Partitioned Data.

Mirko Polato, Alberto Gallinaro, Fabio Aiolli

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

2020

Learning deep kernels in the space of monotone conjunctive polynomials.

Ivano Lauriola, Mirko Polato, Fabio Aiolli

Pattern Recognit. Lett., 2020

Conditioned Variational Autoencoder for top-N item recommendation.

Mirko Polato, Tommaso Carraro, Fabio Aiolli

CoRR, 2020

Recency Aware Collaborative Filtering for Next Basket Recommendation.

Guglielmo Faggioli, Mirko Polato, Fabio Aiolli

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 2020

A Look Inside the Black-Box: Towards the Interpretability of Conditioned Variational Autoencoder for Collaborative Filtering.

Tommaso Carraro, Mirko Polato, Fabio Aiolli

Proceedings of the Adjunct Publication of the 28th ACM Conference on User Modeling, 2020

Big Enough to Care Not Enough to Scare! Crawling to Attack Recommender Systems.

Fabio Aiolli, Mauro Conti, Stjepan Picek, Mirko Polato

Proceedings of the Computer Security - ESORICS 2020, 2020

2019

Boolean kernels for rule based interpretation of support vector machines.

Mirko Polato, Fabio Aiolli

Neurocomputing, 2019

Tag-Based User Profiling: A Game Theoretic Approach.

Guglielmo Faggioli, Mirko Polato, Fabio Aiolli

Proceedings of the Adjunct Publication of the 27th Conference on User Modeling, 2019

Mind your wallet's privacy: identifying Bitcoin wallet apps and user's actions through network traffic analysis.

Fabio Aiolli, Mauro Conti, Ankit Gangwal, Mirko Polato

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 2019

A Preference-Learning Framework for Modeling Relational Data.

Ivano Lauriola, Mirko Polato, Guglielmo Faggioli, Fabio Aiolli

Proceedings of the Recent Advances in Big Data and Deep Learning, 2019

Playing the Large Margin Preference Game.

Mirko Polato, Guglielmo Faggioli, Ivano Lauriola, Fabio Aiolli

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019: Deep Learning, 2019

Evaluation of Tag Clusterings for User Profiling in Movie Recommendation.

Guglielmo Faggioli, Mirko Polato, Ivano Lauriola, Fabio Aiolli

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019 - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, 2019

Interpretable Preference Learning: A Game Theoretic Framework for Large Margin On-Line Feature and Rule Learning.

Mirko Polato, Fabio Aiolli

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Boolean kernels for collaborative filtering in top-N item recommendation.

Mirko Polato, Fabio Aiolli

Neurocomputing, 2018

A Novel Boolean Kernels Family for Categorical Data.

Mirko Polato, Ivano Lauriola, Fabio Aiolli

Entropy, 2018

Time and activity sequence prediction of business process instances.

Mirko Polato, Alessandro Sperduti, Andrea Burattin, Massimiliano de Leoni

Computing, 2018

Efficient Similarity Based Methods For The Playlist Continuation Task.

Guglielmo Faggioli, Mirko Polato, Fabio Aiolli

Proceedings of the ACM Recommender Systems Challenge, 2018

A Game-Theoretic Framework for Interpretable Preference and Feature Learning.

Mirko Polato, Fabio Aiolli

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018

Learning Preferences for Large Scale Multi-label Problems.

Ivano Lauriola, Mirko Polato, Alberto Lavelli, Fabio Rinaldi, Fabio Aiolli

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018

Boolean kernels for interpretable kernel machines.

Mirko Polato, Fabio Aiolli

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

The minimum effort maximum output principle applied to Multiple Kernel Learning.

Ivano Lauriola, Mirko Polato, Fabio Aiolli

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

2017

Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation.

Mirko Polato, Fabio Aiolli

Neurocomputing, 2017

LSTM networks for data-aware remaining time prediction of business process instances.

Nicolò Navarin, Beatrice Vincenzi, Mirko Polato, Alessandro Sperduti

Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence, 2017

Disjunctive Boolean Kernels-based Collaborative Filtering for top-N Item Recommendation.

Mirko Polato, Fabio Aiolli

Proceedings of the 8th Italian Information Retrieval Workshop, 2017

Classification of Categorical Data in the Feature Space of Monotone DNFs.

Mirko Polato, Ivano Lauriola, Fabio Aiolli

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2017, 2017

Radius-Margin Ratio Optimization for Dot-Product Boolean Kernel Learning.

Ivano Lauriola, Mirko Polato, Fabio Aiolli

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2017, 2017

2016

Disjunctive Boolean Kernels for Collaborative Filtering in Top-N Recommendation.

Mirko Polato, Fabio Aiolli

CoRR, 2016

A preliminary study on a recommender system for the job recommendation challenge.

Mirko Polato, Fabio Aiolli

Proceedings of the 2016 Recommender Systems Challenge, 2016

Kernel based collaborative filtering for very large scale top-N item recommendation.

Fabio Aiolli, Mirko Polato

Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

2014

Data-aware remaining time prediction of business process instances.

Mirko Polato, Alessandro Sperduti, Andrea Burattin, Massimiliano de Leoni

Proceedings of the 2014 International Joint Conference on Neural Networks, 2014