Mirko Polato, PhD
Title
Cited by
Cited by
Year
Time and activity sequence prediction of business process instances
M Polato, A Sperduti, A Burattin, M de Leoni
Computing 100 (9), 1005-1031, 2018
812018
Data-aware remaining time prediction of business process instances
M Polato, A Sperduti, A Burattin, M de Leoni
2014 International Joint Conference on Neural Networks (IJCNN), 816-823, 2014
682014
LSTM networks for data-aware remaining time prediction of business process instances
N Navarin, B Vincenzi, M Polato, A Sperduti
2017 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2017
362017
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation
M Polato, F Aiolli
Neurocomputing 268, 17-26, 2017
152017
Boolean kernels for collaborative filtering in top-N item recommendation
M Polato, F Aiolli
Neurocomputing 286, 214-225, 2018
122018
Radius-margin ratio optimization for dot-product boolean kernel learning
I Lauriola, M Polato, F Aiolli
International conference on artificial neural networks, 183-191, 2017
112017
Kernel based collaborative filtering for very large scale top-n item recommendation
M Polato, F Aiolli
Proceedings of the European Symposium on Artificial Neural Networks …, 2016
102016
Mind your wallet's privacy: identifying Bitcoin wallet apps and user's actions through network traffic analysis
F Aiolli, M Conti, A Gangwal, M Polato
Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 1484-1491, 2019
72019
A preliminary study on a recommender system for the job recommendation challenge
M Polato, F Aiolli
Proceedings of the Recommender Systems Challenge 2016, 2016
72016
A novel boolean kernels family for categorical data
M Polato, I Lauriola, F Aiolli
Entropy 20 (6), 444, 2018
52018
The minimum effort maximum output principle applied to Multiple Kernel Learning.
I Lauriola, M Polato, F Aiolli
ESANN, 2018
42018
Classification of categorical data in the feature space of monotone dnfs
M Polato, I Lauriola, F Aiolli
International Conference on Artificial Neural Networks, 279-286, 2017
42017
Tag-based user profiling: a game theoretic approach
G Faggioli, M Polato, F Aiolli
Adjunct Publication of the 27th Conference on User Modeling, Adaptation and …, 2019
32019
Learning with subsampled kernel-based methods: Environmental and financial applications
MA Shahrokhabadi, A Neisy, E Perracchione, M Polato
Dolomites Research Notes on Approximation 12 (1), 2019
32019
Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning
M Polato, F Aiolli
33rd AAAI Conference on Artificial Intelligence, 2019
32019
Boolean kernels for rule based interpretation of support vector machines
M Polato, F Aiolli
Neurocomputing 342, 113-124, 2019
22019
Efficient similarity based methods for the playlist continuation task
G Faggioli, M Polato, F Aiolli
Proceedings of the ACM Recommender Systems Challenge 2018, 1-6, 2018
22018
Recency aware collaborative filtering for next basket recommendation
G Faggioli, M Polato, F Aiolli
Proceedings of the 28th ACM Conference on User Modeling, Adaptation and …, 2020
12020
Learning preferences for large scale multi-label problems
I Lauriola, M Polato, A Lavelli, F Rinaldi, F Aiolli
International Conference on Artificial Neural Networks, 546-555, 2018
12018
A game-theoretic framework for interpretable preference and feature learning
M Polato, F Aiolli
International Conference on Artificial Neural Networks, 659-668, 2018
12018
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Articles 1–20