Mirko Polato

发表

Classification of Categorical Data in the Feature Space of Monotone DNFs

Mirko Polato, Ivano Lauriola, Fabio Aiolli, 2017, ICANN.

Model-free predictive current control for a SynRM drive based on an effective update of measured current responses

Silverio Bolognani, Mirko Polato, Davide Da Rù, 2017, 2017 IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE).

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

Mirko Polato, Fabio Aiolli, 2017, IIR.

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

Mauro Conti, Mirko Polato, Fabio Aiolli, 2019, SAC.

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

Mauro Conti, Stjepan Picek, Mirko Polato, 2020, ESORICS.

Conditioned Variational Autoencoder for top-N item recommendation pdf

Mirko Polato, Fabio Aiolli, Tommaso Carraro, 2020, ICANN.

Boolean kernels for rule based interpretation of support vector machines

Mirko Polato, Fabio Aiolli, F. Aiolli, 2019, Neurocomputing.

Time and activity sequence prediction of business process instances

Alessandro Sperduti, Massimiliano de Leoni, Mirko Polato, 2016, Computing.

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

Mirko Polato, Fabio Aiolli, Tommaso Carraro, 2020, UMAP.

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

Mirko Polato, Fabio Aiolli, 2016, ESANN.

Boolean kernels for interpretable kernel machines

Mirko Polato, Fabio Aiolli, F. Aiolli, 2018, ESANN.

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

Mirko Polato, Ivano Lauriola, Fabio Aiolli, 2017, ICANN.

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

Mirko Polato, Fabio Aiolli, 2016, ArXiv.

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

Alessandro Sperduti, Mirko Polato, Nicolò Navarin, 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

Data-aware remaining time prediction of business process instances

Alessandro Sperduti, Massimiliano de Leoni, Mirko Polato, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

Playing the Large Margin Preference Game

Mirko Polato, Ivano Lauriola, Fabio Aiolli, 2019, ICANN.

Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning

Mirko Polato, Fabio Aiolli, 2019, AAAI.

Boolean kernels for collaborative filtering in top-N item recommendation

Mirko Polato, Fabio Aiolli, 2018, Neurocomputing.

Learning Preferences for Large Scale Multi-label Problems

Fabio Rinaldi, Alberto Lavelli, Mirko Polato, 2018, ICANN.

Evaluation of Tag Clusterings for User Profiling in Movie Recommendation

Mirko Polato, Ivano Lauriola, Fabio Aiolli, 2019, ICANN.

Mirko Polato, Fabio Aiolli, Guglielmo Faggioli, 2018, RecSys Challenge.

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

Mirko Polato, Fabio Aiolli, F. Aiolli, 2016, RecSys Challenge '16.

Recency Aware Collaborative Filtering for Next Basket Recommendation

Mirko Polato, Fabio Aiolli, Guglielmo Faggioli, 2020, UMAP.

A Novel Boolean Kernels Family for Categorical Data †

Mirko Polato, Ivano Lauriola, Fabio Aiolli, 2018, Entropy.

A Preference-Learning Framework for Modeling Relational Data

Mirko Polato, Ivano Lauriola, Fabio Aiolli, 2019, INNSBDDL.

The minimum effort maximum output principle applied to Multiple Kernel Learning

Mirko Polato, Ivano Lauriola, Fabio Aiolli, 2018, ESANN.

Tag-Based User Profiling: A Game Theoretic Approach

Mirko Polato, Fabio Aiolli, Guglielmo Faggioli, 2019, UMAP.

A Game-Theoretic Framework for Interpretable Preference and Feature Learning

Mirko Polato, Fabio Aiolli, 2018, ICANN.

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

Mirko Polato, Fabio Aiolli, F. Aiolli, 2016, Neurocomputing.

Learning deep kernels in the space of monotone conjunctive polynomials

Mirko Polato, Ivano Lauriola, Fabio Aiolli, 2020, Pattern Recognit. Lett..

Definition and learning of logic-based kernels for categorical data, and application to collaborative filtering

Mirko Polato, 2018 .

Radical scavenging activity of natural antioxidants and drugs: Development of a combined machine learning and quantum chemistry protocol.

Mirko Polato, Fabio Aiolli, Laura Orian, 2020, The Journal of chemical physics.

Propositional Kernels

Mirko Polato, Fabio Aiolli, F. Aiolli, 2021, Entropy.

Privacy-Preserving Kernel Computation For Vertically Partitioned Data

Mirko Polato, Fabio Aiolli, Alberto Gallinaro, 2021, ESANN 2021 proceedings.