Mirko Polato

Assistant Professor University of Turin, Department of Computer Science

mak_profile.jpeg
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

  1. NEUCOMJ16
    Improving Rule-Based Classifiers by Bayes Point Aggregation
    Luca Bergamin, Fabio Aiolli
    Neurocomputing 2024
  2. TSCJ15
    PriVeriFL: Privacy-Preserving and Aggregation-Verifiable Federated Learning
    Transactions on Services Computing 2024
  3. CSURJ14
    A Survey on Hypergraph Representation Learning
    Antelmi, Alessia, Cordasco, Gennaro,  Polato, Mirko, Scarano, Vittorio, Spagnuolo, Carmine, and Yang, Dingqi
    ACM Comput. Surv. 2023
  4. 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
  5. NEUCOMJ12
    PRL: A game theoretic large margin method for interpretable feature learning
    Polato, M., Faggioli, G., and Aiolli, F.
    Neurocomputing 2022
  6. IJCNNC20
    Federated Variational Autoencoder for Collaborative Filtering
    Polato, M.
    In International Joint Conference on Neural Networks 2021
  7. PATRECJ08
    Learning deep kernels in the space of monotone conjunctive polynomials
    Lauriola, I.,  Polato, M., and Aiolli, F.
    Pattern Recognition Letters 2020
  8. 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
  9. 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
  10. 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