Mirko Polato

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 51

Top Topics

Item Recommendation

Collaborative Filtering

Top Venues

Publications

Sunwoo Kim, Soo Yong Lee, Yue Gao, Alessia Antelmi, Mirko Polato, Kijung Shin
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide. CoRR (2024)
Alessia Antelmi, Gennaro Cordasco, Mirko Polato, Vittorio Scarano, Carmine Spagnuolo, Dingqi Yang
A Survey on Hypergraph Representation Learning. ACM Comput. Surv. 56 (1) (2024)
Sunwoo Kim, Soo Yong Lee, Yue Gao, Alessia Antelmi, Mirko Polato, Kijung Shin
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide. KDD (2024)
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
Experimenting with Emerging ARM and RISC-V Systems for Decentralised Machine Learning. CoRR (2023)
Roberto Esposito, Mirko Polato, Marco Aldinucci
Boosting Methods for Federated Learning. SEBD (2023)
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
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning. CF (2023)
Mirko Polato, Roberto Esposito, Walter Riviera, Zenglin Xu, Irwin King
1st Workshop on Federated Learning Technologies. WWW (Companion Volume) (2023)
Mirko Polato, Roberto Esposito, Marco Aldinucci
Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent. IJCNN (2022)
Valentina Fietta, Francesca Zecchinato, Brigida Di Stasi, Mirko Polato, Merylin Monaro
Dissociation Between Users' Explicit and Implicit Attitudes Toward Artificial Intelligence: An Experimental Study. IEEE Trans. Hum. Mach. Syst. 52 (3) (2022)
Tommaso Carraro, Mirko Polato, Luca Bergamin, Fabio Aiolli
Conditioned Variational Autoencoder for Top-N Item Recommendation. ICANN (2) (2022)
Fabio Aiolli, Mauro Conti, Stjepan Picek, Mirko Polato
On the feasibility of crawling-based attacks against recommender systems. J. Comput. Secur. 30 (4) (2022)
Mirko Polato, Fabio Aiolli, Luca Bergamin, Tommaso Carraro
Bayes Point Rule Set Learning. ESANN (2022)
Mirko Polato, Guglielmo Faggioli, Fabio Aiolli
PRL: A game theoretic large margin method for interpretable feature learning. Neurocomputing 479 (2022)
Luca Bergamin, Tommaso Carraro, Mirko Polato, Fabio Aiolli
Novel Applications for VAE-based Anomaly Detection Systems. IJCNN (2022)
Fabio Aiolli, Luca Bergamin, Tommaso Carraro, Mirko Polato
Bayes Point Rule Set Learning. CoRR (2022)
Luca Bergamin, Tommaso Carraro, Mirko Polato, Fabio Aiolli
Novel Applications for VAE-based Anomaly Detection Systems. CoRR (2022)
Mirko Polato, Alberto Gallinaro, Fabio Aiolli
Privacy-Preserving Kernel Computation For Vertically Partitioned Data. ESANN (2021)
Mirko Polato, Fabio Aiolli
Propositional Kernels. Entropy 23 (8) (2021)
Mirko Polato, Denys Demchenko, Almat Kuanyshkereyev, Nicolò Navarin
Efficient Multilingual Deep Learning Model for Keyword Categorization. SSCI (2021)
Guglielmo Faggioli, Mirko Polato, Fabio Aiolli
Recency Aware Collaborative Filtering for Next Basket Recommendation. UMAP (2020)
Tommaso Carraro, Mirko Polato, Fabio Aiolli
A Look Inside the Black-Box: Towards the Interpretability of Conditioned Variational Autoencoder for Collaborative Filtering. UMAP (Adjunct Publication) (2020)
Ivano Lauriola, Mirko Polato, Fabio Aiolli
Learning deep kernels in the space of monotone conjunctive polynomials. Pattern Recognit. Lett. 140 (2020)
Mirko Polato, Tommaso Carraro, Fabio Aiolli
Conditioned Variational Autoencoder for top-N item recommendation. CoRR (2020)
Fabio Aiolli, Mauro Conti, Stjepan Picek, Mirko Polato
Big Enough to Care Not Enough to Scare! Crawling to Attack Recommender Systems. ESORICS (2) (2020)
Mirko Polato, Fabio Aiolli
Boolean kernels for rule based interpretation of support vector machines. Neurocomputing 342 (2019)
Fabio Aiolli, Mauro Conti, Ankit Gangwal, Mirko Polato
Mind your wallet's privacy: identifying Bitcoin wallet apps and user's actions through network traffic analysis. SAC (2019)
Mirko Polato, Fabio Aiolli
Interpretable Preference Learning: A Game Theoretic Framework for Large Margin On-Line Feature and Rule Learning. AAAI (2019)
Mirko Polato, Guglielmo Faggioli, Ivano Lauriola, Fabio Aiolli
Playing the Large Margin Preference Game. ICANN (2) (2019)
Guglielmo Faggioli, Mirko Polato, Ivano Lauriola, Fabio Aiolli
Evaluation of Tag Clusterings for User Profiling in Movie Recommendation. ICANN (Workshop) (2019)
Ivano Lauriola, Mirko Polato, Guglielmo Faggioli, Fabio Aiolli
A Preference-Learning Framework for Modeling Relational Data. INNSBDDL (2019)
Guglielmo Faggioli, Mirko Polato, Fabio Aiolli
Tag-Based User Profiling: A Game Theoretic Approach. UMAP (Adjunct Publication) (2019)
Mirko Polato, Fabio Aiolli
Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning. CoRR (2018)
Ivano Lauriola, Mirko Polato, Fabio Aiolli
The minimum effort maximum output principle applied to Multiple Kernel Learning. ESANN (2018)
Mirko Polato, Alessandro Sperduti, Andrea Burattin, Massimiliano de Leoni
Time and activity sequence prediction of business process instances. Computing 100 (9) (2018)
Mirko Polato, Fabio Aiolli
Boolean kernels for collaborative filtering in top-N item recommendation. Neurocomputing 286 (2018)
Mirko Polato, Fabio Aiolli
Boolean kernels for interpretable kernel machines. ESANN (2018)
Ivano Lauriola, Mirko Polato, Alberto Lavelli, Fabio Rinaldi, Fabio Aiolli
Learning Preferences for Large Scale Multi-label Problems. ICANN (1) (2018)
Guglielmo Faggioli, Mirko Polato, Fabio Aiolli
Efficient Similarity Based Methods For The Playlist Continuation Task. RecSys Challenge (2018)
Mirko Polato, Ivano Lauriola, Fabio Aiolli
A Novel Boolean Kernels Family for Categorical Data. Entropy 20 (6) (2018)
Mirko Polato, Fabio Aiolli
A Game-Theoretic Framework for Interpretable Preference and Feature Learning. ICANN (1) (2018)
Mirko Polato, Fabio Aiolli
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation. Neurocomputing 268 (2017)
Nicolò Navarin, Beatrice Vincenzi, Mirko Polato, Alessandro Sperduti
LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances. CoRR (2017)
Nicolò Navarin, Beatrice Vincenzi, Mirko Polato, Alessandro Sperduti
LSTM networks for data-aware remaining time prediction of business process instances. SSCI (2017)
Ivano Lauriola, Mirko Polato, Fabio Aiolli
Radius-Margin Ratio Optimization for Dot-Product Boolean Kernel Learning. ICANN (2) (2017)
Mirko Polato, Fabio Aiolli
Disjunctive Boolean Kernels-based Collaborative Filtering for top-N Item Recommendation. IIR (2017)
Mirko Polato, Ivano Lauriola, Fabio Aiolli
Classification of Categorical Data in the Feature Space of Monotone DNFs. ICANN (2) (2017)
Mirko Polato, Fabio Aiolli
A preliminary study on a recommender system for the job recommendation challenge. RecSys Challenge (2016)
Mirko Polato, Fabio Aiolli
Disjunctive Boolean Kernels for Collaborative Filtering in Top-N Recommendation. CoRR (2016)
Mirko Polato, Alessandro Sperduti, Andrea Burattin, Massimiliano de Leoni
Time and Activity Sequence Prediction of Business Process Instances. CoRR (2016)
Fabio Aiolli, Mirko Polato
Kernel based collaborative filtering for very large scale top-N item recommendation. ESANN (2016)
Mirko Polato, Fabio Aiolli
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation. CoRR (2016)
Mirko Polato, Alessandro Sperduti, Andrea Burattin, Massimiliano de Leoni
Data-aware remaining time prediction of business process instances. IJCNN (2014)