Mirko Polato

Email address

[name].[surname]@unito.it

Office address

Office A1 (1st Floor)

Corso Svizzera 185

10149, Torino (TO), Italy

Hi there! My name is Mirko Polato and I am an Assistant Professor in the Department of Computer Science at the University of Turin (Italy).

I received my MSc and Ph.D. in Brain, Mind, and Computer Science from the University of Padova (Italy) in 2013 and 2018, respectively. In 2017, I was a visiting Ph.D. student at the Delft University of Technology in the Multimedia Computing group. From 2018 to 2021, I have spent three years as a post-doctoral fellow at the University of Padova.

Currently, I have published 40+ research articles on international conferences and journals. I served as a Program Committee member of several international conferences and as a guest editor and referee for several international journals.

My main research interests include federated learning, interpretable machine learning, cybersecurity, recommender system, and representation learning.

news

Oct 1, 2024	Our paper Improving Rule-Based Classifiers by Bayes Point Aggregation has been accepted for publication in the journal Neurocomputing.
Aug 21, 2024	Our paper PriVeriFL: Privacy-Preserving and Aggregation-Verifiable Federated Learning has been accepted for publication in the journal IEEE Transactions on Services Computing.
Jun 13, 2024	Our paper FedHP: Federated Learning with Hyperspherical Prototypical Regularization has been accepted for presentation at ESANN 2024.
Jun 13, 2024	Our paper Vision Language Models as Policy Learners in Reinforcement Learning Environments has been accepted as a poster at ESANN 2024.
May 24, 2024	Our lecture style tutorial Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide has been accepted at KDD 2024. Check out our survey paper https://arxiv.org/abs/2404.01039!

selected publications

NEUCOMJ16

Improving Rule-Based Classifiers by Bayes Point Aggregation

Luca Bergamin, Fabio Aiolli

Neurocomputing 2024

Bib

@article{Bergamin2024neuro,
  author = {Luca Bergamin, Mirko Polato, Fabio Aiolli},
  title = {Improving Rule-Based Classifiers by Bayes Point Aggregation},
  year = {2024},
  publisher = {Elsevier},
  journal = {Neurocomputing},
}

TSCJ15

PriVeriFL: Privacy-Preserving and Aggregation-Verifiable Federated Learning

Transactions on Services Computing 2024

Bib

@article{Lulu2024tsc,
  author = {},
  title = {PriVeriFL: Privacy-Preserving and Aggregation-Verifiable Federated Learning},
  year = {2024},
  publisher = {IEEE},
  journal = {Transactions on Services Computing},
  volume = {},
  number = {},
  issn = {1939-1374},
  pages = {1-16},
  doi = {10.1109/TSC.2024.3451183},
  address = {Los Alamitos, CA, USA},
}

CSURJ14

A Survey on Hypergraph Representation Learning

Antelmi, Alessia, Cordasco, Gennaro, Polato, Mirko, Scarano, Vittorio, Spagnuolo, Carmine, and Yang, Dingqi

ACM Comput. Surv. 2023

Bib

@article{Antelmi2023csur,
  author = {Antelmi, Alessia and Cordasco, Gennaro and Polato, Mirko and Scarano, Vittorio and Spagnuolo, Carmine and Yang, Dingqi},
  title = {A Survey on Hypergraph Representation Learning},
  year = {2023},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  issn = {0360-0300},
  url = {https://doi.org/10.1145/3605776},
  doi = {10.1145/3605776},
  journal = {ACM Comput. Surv.},
  keywords = {Hypergraph Attention, Hypergraph Neural Networks, Hypergraph Representation Learning, Hypergraph Convolution, Hypergraph Embedding},
}

IJCNNC21

Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent

Polato, M., Esposito, R., and Aldinucci, M.

In International Joint Conference on Neural Networks 2022

Bib Code

@inproceedings{Polato2022ijcnn,
  author = {Polato, M. and Esposito, R. and Aldinucci, M.},
  title = {Boosting the Federation: Cross-Silo Federated Learning without Gradient Descent},
  booktitle = {International Joint Conference on Neural Networks},
  year = {2022},
  volume = {},
  doi = {10.1109/IJCNN55064.2022.9892284},
  url = {},
  document_type = {Conference Paper},
  source = {},
  note = {},
  code = {https://github.com/ml-unito/federation_boosting},
}

NEUCOMJ12

PRL: A game theoretic large margin method for interpretable feature learning

Polato, M., Faggioli, G., and Aiolli, F.

Neurocomputing 2022

Bib HTML Code

@article{Polato2022neucom,
  author = {Polato, M. and Faggioli, G. and Aiolli, F.},
  title = {PRL: A game theoretic large margin method for interpretable feature learning},
  journal = {Neurocomputing},
  year = {2022},
  volume = {479},
  pages = {106-120},
  doi = {10.1016/j.neucom.2022.01.016},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123636162&doi=10.1016%2fj.neucom.2022.01.016&partnerID=40&md5=4bac852c5560981ca25a09c88fe2c1a9},
  document_type = {Article},
  source = {Scopus},
  code = {https://github.com/makgyver/PRL},
  html = {https://www.sciencedirect.com/science/article/pii/S0925231222000339},
}

IJCNNC20

Federated Variational Autoencoder for Collaborative Filtering

Polato, M.

In International Joint Conference on Neural Networks 2021

Bib HTML Code

@inproceedings{Polato2021ijcnn,
  author = {Polato, M.},
  title = {Federated Variational Autoencoder for Collaborative Filtering},
  booktitle = {International Joint Conference on Neural Networks},
  year = {2021},
  volume = {2021-July},
  doi = {10.1109/IJCNN52387.2021.9533358},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116461340&doi=10.1109%2fIJCNN52387.2021.9533358&partnerID=40&md5=fd064184a372bcc1b09e3ee1d2fda604},
  document_type = {Conference Paper},
  source = {Scopus},
  code = {https://colab.research.google.com/drive/1sOx8QZYwRoEvOc6kBiQnVqZNVdbAEofM?usp=sharing},
  html = {https://ieeexplore.ieee.org/abstract/document/9533358},
}

PATRECJ08

Learning deep kernels in the space of monotone conjunctive polynomials

Lauriola, I., Polato, M., and Aiolli, F.

Pattern Recognition Letters 2020

Bib HTML Code

@article{Lauriola2020patrec,
  author = {Lauriola, I. and Polato, M. and Aiolli, F.},
  title = {Learning deep kernels in the space of monotone conjunctive polynomials},
  journal = {Pattern Recognition Letters},
  year = {2020},
  volume = {140},
  pages = {200-206},
  doi = {10.1016/j.patrec.2020.10.013},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093985808&doi=10.1016%2fj.patrec.2020.10.013&partnerID=40&md5=ac677a2d73e0ce9db10672102d4553aa},
  document_type = {Article},
  source = {Scopus},
  html = {https://www.sciencedirect.com/science/article/pii/S0167865520304013},
  code = {https://github.com/IvanoLauriola/MKLpy},
}

UMAPC17

Recency Aware Collaborative Filtering for Next Basket Recommendation

Faggioli, G., Polato, M., and Aiolli, F.

In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization 2020

Bib HTML Code

@inproceedings{Faggioli202080,
  author = {Faggioli, G. and Polato, M. and Aiolli, F.},
  title = {Recency Aware Collaborative Filtering for Next Basket Recommendation},
  booktitle = {Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization},
  year = {2020},
  pages = {80-87},
  doi = {10.1145/3340631.3394850},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089352467&doi=10.1145%2f3340631.3394850&partnerID=40&md5=49092a855ef9f1928ac126d40bd46019},
  document_type = {Conference Paper},
  source = {Scopus},
  code = {https://github.com/MayloIFERR/RACF},
  html = {https://dl.acm.org/doi/abs/10.1145/3340631.3394850},
}

ESORICSC16

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

Aiolli, F., Conti, M., Picek, S., and Polato, M.

In 25th European Symposium on Research in Computer Security, ESORICS 2020 2020

Bib HTML

@inproceedings{Aiolli2020esorics,
  author = {Aiolli, F. and Conti, M. and Picek, S. and Polato, M.},
  title = {Big Enough to Care Not Enough to Scare! Crawling to Attack Recommender Systems},
  year = {2020},
  isbn = {978-3-030-59012-3},
  publisher = {Springer-Verlag},
  address = {Berlin, Heidelberg},
  url = {https://doi.org/10.1007/978-3-030-59013-0_9},
  doi = {10.1007/978-3-030-59013-0_9},
  booktitle = {25th European Symposium on Research in Computer Security, ESORICS 2020},
  pages = {165–184},
  numpages = {20},
  keywords = {Crawling, Recommender systems, Shilling attack, Security, Collaborative filtering},
  location = {Guildford, United Kingdom},
  html = {https://dl.acm.org/doi/abs/10.1007/978-3-030-59013-0_9},
}

AAAIC15

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

Polato, M., and Aiolli, F.

In 33rd AAAI Conference on Artificial Intelligence 2019

Bib HTML Code

@inproceedings{Polato2019aaai,
  author = {Polato, M. and Aiolli, F.},
  title = {Interpretable preference learning: A game theoretic framework for large margin on-line feature and rule learning},
  booktitle = {33rd AAAI Conference on Artificial Intelligence},
  year = {2019},
  pages = {4723-4730},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090808191&partnerID=40&md5=dcf1bd412a92da6087a84ff0ca1f9347},
  document_type = {Conference Paper},
  source = {Scopus},
  code = {https://github.com/makgyver/PRL},
  html = {https://ojs.aaai.org/index.php/AAAI/article/view/4398/4276},
}