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Mirko Polato
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2020 – today
- 2025
- [j12]Luca Bergamin
, Mirko Polato, Fabio Aiolli
Improving rule-based classifiers by Bayes point aggregation. Neurocomputing 613: 128699 (2025) - 2024
- [j11]Alessia Antelmi
A Survey on Hypergraph Representation Learning. ACM Comput. Surv. 56(1): 24:1-24:38 (2024) - [c32]Sunwoo Kim
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide. KDD 2024: 6534-6544 - [i10]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 abs/2404.01039 (2024) - 2023
- [c31]Gianluca Mittone
Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning. CF 2023: 73-83 - [c30]Roberto Esposito, Mirko Polato, Marco Aldinucci:
Boosting Methods for Federated Learning. SEBD 2023: 439-448 - [c29]Mirko Polato
1st Workshop on Federated Learning Technologies. WWW (Companion Volume) 2023: 1150 - [i9]Gianluca Mittone, Nicolò Tonci, Robert Birke, Iacopo Colonnelli
Experimenting with Emerging ARM and RISC-V Systems for Decentralised Machine Learning. CoRR abs/2302.07946 (2023) - 2022
- [j10]Mirko Polato, Guglielmo Faggioli
PRL: A game theoretic large margin method for interpretable feature learning. Neurocomputing 479: 106-120 (2022) - [j9]Fabio Aiolli, Mauro Conti
On the feasibility of crawling-based attacks against recommender systems. J. Comput. Secur. 30(4): 599-621 (2022) - [j8]Valentina Fietta
Dissociation Between Users' Explicit and Implicit Attitudes Toward Artificial Intelligence: An Experimental Study. IEEE Trans. Hum. Mach. Syst. 52(3): 481-489 (2022) - [c28]Mirko Polato, Fabio Aiolli, Luca Bergamin, Tommaso Carraro:
Bayes Point Rule Set Learning. ESANN 2022 - [c27]Tommaso Carraro
Conditioned Variational Autoencoder for Top-N Item Recommendation. ICANN (2) 2022: 785-796 - [c26]Luca Bergamin, Tommaso Carraro, Mirko Polato, Fabio Aiolli:
Novel Applications for VAE-based Anomaly Detection Systems. IJCNN 2022: 1-8 - [c25]Mirko Polato, Roberto Esposito
Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent. IJCNN 2022: 1-10 - [i8]Fabio Aiolli, Luca Bergamin, Tommaso Carraro, Mirko Polato:
Bayes Point Rule Set Learning. CoRR abs/2204.05251 (2022) - [i7]Luca Bergamin, Tommaso Carraro, Mirko Polato, Fabio Aiolli:
Novel Applications for VAE-based Anomaly Detection Systems. CoRR abs/2204.12577 (2022) - 2021
- [j7]Mirko Polato
Propositional Kernels. Entropy 23(8): 1020 (2021) - [c24]Mirko Polato, Alberto Gallinaro, Fabio Aiolli:
Privacy-Preserving Kernel Computation For Vertically Partitioned Data. ESANN 2021 - [c23]Mirko Polato:
Federated Variational Autoencoder for Collaborative Filtering. IJCNN 2021: 1-8 - [c22]Mirko Polato, Denys Demchenko, Almat Kuanyshkereyev, Nicolò Navarin
Efficient Multilingual Deep Learning Model for Keyword Categorization. SSCI 2021: 1-8 - 2020
- [j6]Ivano Lauriola
Learning deep kernels in the space of monotone conjunctive polynomials. Pattern Recognit. Lett. 140: 200-206 (2020) - [c21]Fabio Aiolli, Mauro Conti
Big Enough to Care Not Enough to Scare! Crawling to Attack Recommender Systems. ESORICS (2) 2020: 165-184 - [c20]Guglielmo Faggioli
Recency Aware Collaborative Filtering for Next Basket Recommendation. UMAP 2020: 80-87 - [c19]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: 233-236 - [i6]Mirko Polato, Tommaso Carraro, Fabio Aiolli:
Conditioned Variational Autoencoder for top-N item recommendation. CoRR abs/2004.11141 (2020)
2010 – 2019
- 2019
- [j5]Mirko Polato
Boolean kernels for rule based interpretation of support vector machines. Neurocomputing 342: 113-124 (2019) - [c18]Mirko Polato, Fabio Aiolli:
Interpretable Preference Learning: A Game Theoretic Framework for Large Margin On-Line Feature and Rule Learning. AAAI 2019: 4723-4730 - [c17]Guglielmo Faggioli
Evaluation of Tag Clusterings for User Profiling in Movie Recommendation. ICANN (Workshop) 2019: 456-468 - [c16]Mirko Polato
Playing the Large Margin Preference Game. ICANN (2) 2019: 792-804 - [c15]Ivano Lauriola, Mirko Polato, Guglielmo Faggioli
A Preference-Learning Framework for Modeling Relational Data. INNSBDDL 2019: 359-369 - [c14]Fabio Aiolli, Mauro Conti
Mind your wallet's privacy: identifying Bitcoin wallet apps and user's actions through network traffic analysis. SAC 2019: 1484-1491 - [c13]Guglielmo Faggioli
Tag-Based User Profiling: A Game Theoretic Approach. UMAP (Adjunct Publication) 2019: 267-271 - 2018
- [j4]Mirko Polato
Time and activity sequence prediction of business process instances. Computing 100(9): 1005-1031 (2018) - [j3]Mirko Polato
A Novel Boolean Kernels Family for Categorical Data. Entropy 20(6): 444 (2018) - [j2]Mirko Polato
Boolean kernels for collaborative filtering in top-N item recommendation. Neurocomputing 286: 214-225 (2018) - [c12]Ivano Lauriola, Mirko Polato, Fabio Aiolli:
The minimum effort maximum output principle applied to Multiple Kernel Learning. ESANN 2018 - [c11]Mirko Polato, Fabio Aiolli:
Boolean kernels for interpretable kernel machines. ESANN 2018 - [c10]Ivano Lauriola
Learning Preferences for Large Scale Multi-label Problems. ICANN (1) 2018: 546-555 - [c9]Mirko Polato
A Game-Theoretic Framework for Interpretable Preference and Feature Learning. ICANN (1) 2018: 659-668 - [c8]Guglielmo Faggioli
Efficient Similarity Based Methods For The Playlist Continuation Task. RecSys Challenge 2018: 15:1-15:6 - [i5]Mirko Polato, Fabio Aiolli:
Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning. CoRR abs/1812.07895 (2018) - 2017
- [j1]Mirko Polato
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation. Neurocomputing 268: 17-26 (2017) - [c7]Ivano Lauriola
Radius-Margin Ratio Optimization for Dot-Product Boolean Kernel Learning. ICANN (2) 2017: 183-191 - [c6]Mirko Polato
Classification of Categorical Data in the Feature Space of Monotone DNFs. ICANN (2) 2017: 279-286 - [c5]Mirko Polato, Fabio Aiolli:
Disjunctive Boolean Kernels-based Collaborative Filtering for top-N Item Recommendation. IIR 2017: 97-100 - [c4]Nicolò Navarin
LSTM networks for data-aware remaining time prediction of business process instances. SSCI 2017: 1-7 - [i4]Nicolò Navarin, Beatrice Vincenzi, Mirko Polato, Alessandro Sperduti:
LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances. CoRR abs/1711.03822 (2017) - 2016
- [c3]Fabio Aiolli, Mirko Polato:
Kernel based collaborative filtering for very large scale top-N item recommendation. ESANN 2016 - [c2]Mirko Polato
A preliminary study on a recommender system for the job recommendation challenge. RecSys Challenge 2016: 1:1-1:4 - [i3]Mirko Polato, Alessandro Sperduti, Andrea Burattin, Massimiliano de Leoni:
Time and Activity Sequence Prediction of Business Process Instances. CoRR abs/1602.07566 (2016) - [i2]Mirko Polato, Fabio Aiolli:
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation. CoRR abs/1612.05729 (2016) - [i1]Mirko Polato, Fabio Aiolli:
Disjunctive Boolean Kernels for Collaborative Filtering in Top-N Recommendation. CoRR abs/1612.07025 (2016) - 2014
- [c1]Mirko Polato
Data-aware remaining time prediction of business process instances. IJCNN 2014: 816-823
Coauthor Index
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