Data-aware remaining time prediction of business process instances. - Mirko Polato; Alessandro Sperduti; Andrea Burattin; Massimiliano de Leoni (IJCNN, 2014)

Kernel based collaborative filtering for very large scale top-N item recommendation. - Fabio Aiolli; Mirko Polato (ESANN, 2016)

A preliminary study on a recommender system for the job recommendation challenge. - Mirko Polato; Fabio Aiolli (RecSys Challenge, 2016)

Disjunctive Boolean Kernels for Collaborative Filtering in Top-N Recommendation. - Mirko Polato; Fabio Aiolli (CoRR, 2016)

Radius-Margin Ratio Optimization for Dot-Product Boolean Kernel Learning. - Ivano Lauriola; Mirko Polato; Fabio Aiolli (ICANN (2), 2017)

Classification of Categorical Data in the Feature Space of Monotone DNFs. - Mirko Polato; Ivano Lauriola; Fabio Aiolli (ICANN (2), 2017)

Disjunctive Boolean Kernels-based Collaborative Filtering for top-N Item Recommendation. - Mirko Polato; Fabio Aiolli (IIR, 2017)

LSTM networks for data-aware remaining time prediction of business process instances. - Nicolò Navarin; Beatrice Vincenzi; Mirko Polato; Alessandro Sperduti (SSCI, 2017)

Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation. - Mirko Polato; Fabio Aiolli (Neurocomputing, 2017)

The minimum effort maximum output principle applied to Multiple Kernel Learning. - Ivano Lauriola; Mirko Polato; Fabio Aiolli (ESANN, 2018)

Boolean kernels for interpretable kernel machines. - Mirko Polato; Fabio Aiolli (ESANN, 2018)

Learning Preferences for Large Scale Multi-label Problems. - Ivano Lauriola; Mirko Polato; Alberto Lavelli; Fabio Rinaldi 001; Fabio Aiolli (ICANN (1), 2018)

A Game-Theoretic Framework for Interpretable Preference and Feature Learning. - Mirko Polato; Fabio Aiolli (ICANN (1), 2018)

Efficient Similarity Based Methods For The Playlist Continuation Task. - Guglielmo Faggioli; Mirko Polato; Fabio Aiolli (RecSys Challenge, 2018)

Time and activity sequence prediction of business process instances. - Mirko Polato; Alessandro Sperduti; Andrea Burattin; Massimiliano de Leoni (Computing, 2018)

A Novel Boolean Kernels Family for Categorical Data. - Mirko Polato; Ivano Lauriola; Fabio Aiolli (Entropy, 2018)

Boolean kernels for collaborative filtering in top-N item recommendation. - Mirko Polato; Fabio Aiolli (Neurocomputing, 2018)

Interpretable Preference Learning: A Game Theoretic Framework for Large Margin On-Line Feature and Rule Learning. - Mirko Polato; Fabio Aiolli (AAAI, 2019)

Evaluation of Tag Clusterings for User Profiling in Movie Recommendation. - Guglielmo Faggioli; Mirko Polato; Ivano Lauriola; Fabio Aiolli (ICANN (Workshop), 2019)

Playing the Large Margin Preference Game. - Mirko Polato; Guglielmo Faggioli; Ivano Lauriola; Fabio Aiolli (ICANN (2), 2019)

A Preference-Learning Framework for Modeling Relational Data. - Ivano Lauriola; Mirko Polato; Guglielmo Faggioli; Fabio Aiolli (INNSBDDL, 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 (SAC, 2019)

Tag-Based User Profiling: A Game Theoretic Approach. - Guglielmo Faggioli; Mirko Polato; Fabio Aiolli (UMAP (Adjunct Publication), 2019)

Boolean kernels for rule based interpretation of support vector machines. - Mirko Polato; Fabio Aiolli (Neurocomputing, 2019)

Big Enough to Care Not Enough to Scare! Crawling to Attack Recommender Systems. - Fabio Aiolli; Mauro Conti; Stjepan Picek; Mirko Polato (ESORICS (2), 2020)

A Look Inside the Black-Box: Towards the Interpretability of Conditioned Variational Autoencoder for Collaborative Filtering. - Tommaso Carraro; Mirko Polato; Fabio Aiolli (UMAP (Adjunct Publication), 2020)

Recency Aware Collaborative Filtering for Next Basket Recommendation. - Guglielmo Faggioli; Mirko Polato; Fabio Aiolli (UMAP, 2020)

Conditioned Variational Autoencoder for top-N item recommendation. - Mirko Polato; Tommaso Carraro; Fabio Aiolli (CoRR, 2020)

Learning deep kernels in the space of monotone conjunctive polynomials. - Ivano Lauriola; Mirko Polato; Fabio Aiolli (Pattern Recognit. Lett., 2020)

Privacy-Preserving Kernel Computation For Vertically Partitioned Data. - Mirko Polato; Alberto Gallinaro; Fabio Aiolli (ESANN, 2021)

Federated Variational Autoencoder for Collaborative Filtering. - Mirko Polato (IJCNN, 2021)

Efficient Multilingual Deep Learning Model for Keyword Categorization. - Mirko Polato; Denys Demchenko; Almat Kuanyshkereyev; Nicolò Navarin (SSCI, 2021)

Propositional Kernels. - Mirko Polato; Fabio Aiolli (Entropy, 2021)

Bayes Point Rule Set Learning. - Mirko Polato; Fabio Aiolli; Luca Bergamin; Tommaso Carraro (ESANN, 2022)

Conditioned Variational Autoencoder for Top-N Item Recommendation. - Tommaso Carraro; Mirko Polato; Luca Bergamin; Fabio Aiolli (ICANN (2), 2022)

Novel Applications for VAE-based Anomaly Detection Systems. - Luca Bergamin; Tommaso Carraro; Mirko Polato; Fabio Aiolli (IJCNN, 2022)

Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent. - Mirko Polato; Roberto Esposito; Marco Aldinucci (IJCNN, 2022)

PRL: A game theoretic large margin method for interpretable feature learning. - Mirko Polato; Guglielmo Faggioli; Fabio Aiolli (Neurocomputing, 2022)

On the feasibility of crawling-based attacks against recommender systems. - Fabio Aiolli; Mauro Conti; Stjepan Picek; Mirko Polato (J. Comput. Secur., 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)

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; et al. (CF, 2023)

Boosting Methods for Federated Learning. - Roberto Esposito; Mirko Polato; Marco Aldinucci (SEBD, 2023)

1st Workshop on Federated Learning Technologies. - Mirko Polato; Roberto Esposito; Walter Riviera; Zenglin Xu; Irwin King (WWW (Companion Volume), 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; et al. (CoRR, 2023)

Vision Language Models as Policy Learners in Reinforcement Learning Environments. - Giovanni Bonetta; Davide Zago; Rossella Cancelliere; Mirko Polato; Bernardo Magnini (ESANN, 2024)

FedHP: Federated Learning with Hyperspherical Prototypical Regularization. - Samuele Fonio; Mirko Polato; Roberto Esposito (ESANN, 2024)

Machine learning in distributed, federated and non-stationary environments - recent trends. - Mirko Polato; Barbara Hammer; Frank-Michael Schleif (ESANN, 2024)

A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide. - Sunwoo Kim 006; Soo Yong Lee; Yue Gao 002; Alessia Antelmi; Mirko Polato; Kijung Shin (KDD, 2024)

fluke: Federated Learning Utility frameworK for Experimentation and research. - Mirko Polato (CoRR, 2024)

A Survey on Hypergraph Representation Learning. - Alessia Antelmi; Gennaro Cordasco; Mirko Polato; Vittorio Scarano; Carmine Spagnuolo; Dingqi Yang (ACM Comput. Surv., 2024)

Hypergraph Motif Representation Learning. - Alessia Antelmi; Gennaro Cordasco; Daniele De Vinco; Valerio Di Pasquale; Mirko Polato; Carmine Spagnuolo (KDD (1), 2025)

SVF: Support Vector Federation. - Mirko Polato; Roberto Esposito; Lorenzo Sciandra (IEEE Access, 2025)

Improving rule-based classifiers by Bayes point aggregation. - Luca Bergamin; Mirko Polato; Fabio Aiolli (Neurocomputing, 2025)

PriVeriFL: Privacy-Preserving and Aggregation-Verifiable Federated Learning. - Lulu Wang 015; Mirko Polato; Alessandro Brighente; Mauro Conti; Lei Zhang 009; Lin Xu 010 (IEEE Trans. Serv. Comput., 2025)