publications | Mirko Polato

2023

CSURJ14
A Survey on Hypergraph Representation Learning

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

ACM Comput. Surv. 2023

Abs Bib

Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in naturally modeling a broad range of systems where high-order relationships exist among their interacting parts. This survey reviews the newly born hypergraph representation learning problem, whose goal is to learn a function to project objects - most commonly nodes - of an input hyper-network into a latent space such that both the structural and relational properties of the network can be encoded and preserved. We provide a thorough overview of existing literature and offer a new taxonomy of hypergraph embedding methods by identifying three main families of techniques, i.e., spectral, proximity-preserving, and (deep) neural networks. For each family, we describe its characteristics and our insights in a single yet flexible framework and then discuss the peculiarities of individual methods, as well as their pros and cons. We then review the main tasks, datasets, and settings in which hypergraph embeddings are typically used. We finally identify and discuss open challenges that would inspire further research in this field.
@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}, note = {Just Accepted}, journal = {ACM Comput. Surv.}, keywords = {Hypergraph Attention, Hypergraph Neural Networks, Hypergraph Representation Learning, Hypergraph Convolution, Hypergraph Embedding}, }

SEBDC26

Boosting Methods for Federated Learning

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

In Symposium on Advanced Database System (to appear) 2023

@inproceedings{Esposito2023sebd,
  author = {Esposito, R. and Polato, M. and Aldinucci, M.},
  title = {Boosting Methods for Federated Learning},
  booktitle = {Symposium on Advanced Database System (to appear)},
  year = {2023},
  pages = {},
  doi = {},
  url = {},
  document_type = {Conference Paper},
}

TheWebConfW05

1st Workshop on Federated Learning Technologies

Polato, M., Esposito, R., Riviera, W., Xu, Z., and King, I.

In Companion Proceedings of the Web Conference 2023

Bib

@inproceedings{Polato2023www,
  author = {Polato, M. and Esposito, R. and Riviera, W. and Xu, Z. and King, I.},
  title = {1st Workshop on Federated Learning Technologies},
  booktitle = {Companion Proceedings of the Web Conference},
  year = {2023},
  pages = {},
  doi = {},
  url = {},
  document_type = {Conference Paper},
}

CFC25

Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning

Mittone, G., Tonci, N., Birke, R., Colonnelli, I., Medic, D., Bartolini, A., Esposito, R., Parisi, E., Beneventi, F., Polato, M., Torquati, M., Benini, L., and Aldinucci, M.

In ACM International Conference on Computing Frontiers 2023

Bib

@inproceedings{Mittone2023cf,
  author = {Mittone, G. and Tonci, N. and Birke, R. and Colonnelli, I. and Medic, D. and Bartolini, A. and Esposito, R. and Parisi, E. and Beneventi, F. and Polato, M. and Torquati, M. and Benini, L. and Aldinucci, M.},
  title = {Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning},
  booktitle = {ACM International Conference on Computing Frontiers},
  year = {2023},
  pages = {},
  doi = {},
  url = {},
  document_type = {Conference Paper},
}

2022

ICANNC24

Conditioned Variational Autoencoder for top-N item recommendation

Carraro, T., Polato, M., Bergamin, L., and Aiolli, F.

In International Conference on Artificial Neural Networks and Machine Learning 2022

Bib

@inproceedings{Carraro2022icann,
  author = {Carraro, T. and Polato, M. and Bergamin, L. and Aiolli, F.},
  title = {Conditioned Variational Autoencoder for top-N item recommendation},
  booktitle = {International Conference on Artificial Neural Networks and Machine Learning},
  year = {2022},
  pages = {785–796},
  doi = {10.1007/978-3-031-15931-2_64},
  url = {},
  document_type = {Conference Paper},
}

ESANNC23

Bayes Point Rule Set Learning

Aiolli, F., Bergamin, L., Carraro, T., and Polato, M.

In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2022

Bib Code

@inproceedings{Aiolli2022esann,
  author = {Aiolli, F. and Bergamin, L. and Carraro, T. and Polato, M.},
  title = {Bayes Point Rule Set Learning},
  booktitle = {European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
  year = {2022},
  volume = {},
  doi = {},
  url = {},
  document_type = {Conference Paper},
  source = {},
  note = {(to appear)},
  code = {https://www.dropbox.com/sh/b8z84jaafdi2tr9/AAA_p3OwWsDoDSQPcuBqmnEGa?dl=0},
}

IJCNNC22

Novel Applications for VAE-based Anomaly Detection Systems

Bergamin, L., Carraro, T., Polato, M., and Aiolli, F.

In International Joint Conference on Neural Networks 2022

Bib Code

@inproceedings{Bergamin2022ijcnn,
  author = {Bergamin, L. and Carraro, T. and Polato, M. and Aiolli, F.},
  title = {Novel Applications for VAE-based Anomaly Detection Systems},
  booktitle = {International Joint Conference on Neural Networks},
  year = {2022},
  volume = {},
  doi = {10.1109/IJCNN55064.2022.9892879},
  url = {},
  document_type = {Conference Paper},
  source = {},
  note = {},
  code = {https://www.dropbox.com/home/Uni/my-works/2022-ijcnn-vabc},
}

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},
}

IEEE THMSJ13

Dissociation Between Users’ Explicit and Implicit Attitudes Toward Artificial Intelligence: An Experimental Study

Fietta, V., Zecchinato, F., Stasi, B.D., Polato, M., and Monaro, M.

IEEE Transactions on Human-Machine Systems 2022

Bib HTML

@article{Fietta2022thms,
  author = {Fietta, V. and Zecchinato, F. and Stasi, B.D. and Polato, M. and Monaro, M.},
  title = {Dissociation Between Users' Explicit and Implicit Attitudes Toward Artificial Intelligence: An Experimental Study},
  journal = {IEEE Transactions on Human-Machine Systems},
  year = {2022},
  volume = {52},
  number = {3},
  pages = {481-489},
  doi = {10.1109/THMS.2021.3125280},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120552515&doi=10.1109%2fTHMS.2021.3125280&partnerID=40&md5=923c278d99dd737b08c859b9b369fa2b},
  document_type = {Article},
  source = {Scopus},
  html = {https://ieeexplore.ieee.org/document/9627595},
}

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},
}

2021

JCSJ11

On the feasibility of crawling-based attacks against recommender systems

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

Journal of Computer Security 2021

Bib HTML

@article{Polato2021jcs,
  author = {Aiolli, F. and Conti, M. and Picek, S. and Polato, M.},
  title = {On the feasibility of crawling-based attacks against recommender systems},
  journal = {Journal of Computer Security},
  year = {2021},
  volume = {31},
  number = {},
  doi = {10.3233/JCS-210041},
  document_type = {Article},
  html = {https://content.iospress.com/articles/journal-of-computer-security/jcs210041},
}

ENTROPYJ10

Propositional kernels

Polato, M., and Aiolli, F.

Entropy 2021

Bib HTML Code

@article{Polato2021entropy,
  author = {Polato, M. and Aiolli, F.},
  title = {Propositional kernels},
  journal = {Entropy},
  year = {2021},
  volume = {23},
  number = {8},
  doi = {10.3390/e23081020},
  art_number = {1020},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112761656&doi=10.3390%2fe23081020&partnerID=40&md5=305c302ed7714a3b169518a09ce87119},
  document_type = {Article},
  source = {Scopus},
  code = {https://github.com/makgyver/propositional_kernels},
  html = {https://www.mdpi.com/1099-4300/23/8/1020},
}

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},
}

ESANNC19

Privacy-Preserving Kernel Computation For Vertically Partitioned Data

Polato, M., Gallinaro, A., and Aiolli, F.

In 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2021

Bib HTML

@inproceedings{Polato2021esann,
  author = {Polato, M. and Gallinaro, A. and Aiolli, F.},
  title = {Privacy-Preserving Kernel Computation For Vertically Partitioned Data},
  booktitle = {29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
  year = {2021},
  pages = {11-16},
  doi = {10.14428/esann/2021.ES2021-152},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129313691&doi=10.14428%2fesann%2f2021.ES2021-152&partnerID=40&md5=75cd6d23311b62c553b65387941006b6},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://www.esann.org/proceedings/2021},
}

SSCIC18

Efficient Multilingual Deep Learning Model for Keyword Categorization

Polato, M., Demchenko, D., Kuanyshkereyev, A., and Navarin, N.

In IEEE Symposium Series on Computational Intelligence 2021

Bib HTML

@inproceedings{Polato2021ssci,
  author = {Polato, M. and Demchenko, D. and Kuanyshkereyev, A. and Navarin, N.},
  title = {Efficient Multilingual Deep Learning Model for Keyword Categorization},
  booktitle = {IEEE Symposium Series on Computational Intelligence},
  year = {2021},
  doi = {10.1109/SSCI50451.2021.9660132},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125808331&doi=10.1109%2fSSCI50451.2021.9660132&partnerID=40&md5=c68c22723fa03742f50440f29ce5182d},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://ieeexplore.ieee.org/document/9660132},
}

OMEGAJ09

Social Support and Help-Seeking Among Suicide Bereaved: A Study With Italian Survivors

Entilli, L., Leo, D.D., Aiolli, F., Polato, M., Gaggi, O., and Cipolletta, S.

Omega (United States) 2021

Bib HTML

@article{Entilli2021omega,
  author = {Entilli, L. and Leo, D.D. and Aiolli, F. and Polato, M. and Gaggi, O. and Cipolletta, S.},
  title = {Social Support and Help-Seeking Among Suicide Bereaved: A Study With Italian Survivors},
  journal = {Omega (United States)},
  year = {2021},
  doi = {10.1177/00302228211024112},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108163313&doi=10.1177%2f00302228211024112&partnerID=40&md5=77de67335af2c012e82a4d1a4fff63c1},
  document_type = {Article},
  source = {Scopus},
  html = {https://journals.sagepub.com/doi/full/10.1177/00302228211024112},
}

2020

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},
}

JCPJ07

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

Muraro, C., Polato, M., Bortoli, M., Aiolli, F., and Orian, L.

Journal of Chemical Physics 2020

Bib HTML

@article{Muraro2020jcp,
  author = {Muraro, C. and Polato, M. and Bortoli, M. and Aiolli, F. and Orian, L.},
  title = {Radical scavenging activity of natural antioxidants and drugs: Development of a combined machine learning and quantum chemistry protocol},
  journal = {Journal of Chemical Physics},
  year = {2020},
  volume = {153},
  number = {11},
  doi = {10.1063/5.0013278},
  art_number = {114117},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091572948&doi=10.1063%2f5.0013278&partnerID=40&md5=c01c18e4870d8253f58567bd565a9fe3},
  document_type = {Article},
  source = {Scopus},
  html = {https://pubmed.ncbi.nlm.nih.gov/32962380/},
}

UMAPW04

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

Carraro, T., Polato, M., and Aiolli, F.

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

Bib HTML

@inproceedings{Carraro2020umap,
  author = {Carraro, T. and Polato, M. and Aiolli, F.},
  title = {A Look Inside the Black-Box: Towards the Interpretability of Conditioned Variational Autoencoder for Collaborative Filtering},
  booktitle = {Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization},
  year = {2020},
  pages = {233-236},
  doi = {10.1145/3386392.3399305},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089267727&doi=10.1145%2f3386392.3399305&partnerID=40&md5=080d7f7cbef2b3a50d01544bd8582fea},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://dl.acm.org/doi/abs/10.1145/3386392.3399305},
}

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},
}

2019

UMAPW03

Tag-based user profiling: A game theoretic approach

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

In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization 2019

Bib HTML

@inproceedings{Faggioli2019umap,
  author = {Faggioli, G. and Polato, M. and Aiolli, F.},
  title = {Tag-based user profiling: A game theoretic approach},
  booktitle = {Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization},
  year = {2019},
  pages = {267-271},
  doi = {10.1145/3314183.3323462},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068685109&doi=10.1145%2f3314183.3323462&partnerID=40&md5=f952c57db1a30cfeecae00fb97d8ab3f},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://dl.acm.org/doi/abs/10.1145/3314183.3323462},
}

NEUCOMJ06

Boolean kernels for rule based interpretation of support vector machines

Polato, M., and Aiolli, F.

Neurocomputing 2019

Bib HTML

@article{Polato2019neucom,
  author = {Polato, M. and Aiolli, F.},
  title = {Boolean kernels for rule based interpretation of support vector machines},
  journal = {Neurocomputing},
  year = {2019},
  volume = {342},
  pages = {113-124},
  doi = {10.1016/j.neucom.2018.11.094},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061737830&doi=10.1016%2fj.neucom.2018.11.094&partnerID=40&md5=941325d1d0788664b92eb7bb13ed93fb},
  document_type = {Article},
  source = {Scopus},
  html = {https://www.sciencedirect.com/science/article/pii/S092523121930150X},
}

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},
}

ICANNC14

Evaluation of Tag Clusterings for User Profiling in Movie Recommendation

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

In International Conference on Artificial Neural Networks and Machine Learning 2019

Bib HTML

@inproceedings{Faggioli2019icann,
  author = {Faggioli, G. and Polato, M. and Lauriola, I. and Aiolli, F.},
  title = {Evaluation of Tag Clusterings for User Profiling in Movie Recommendation},
  booktitle = {International Conference on Artificial Neural Networks and Machine Learning},
  year = {2019},
  pages = {456-468},
  doi = {10.1007/978-3-030-30493-5_45},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072973857&doi=10.1007%2f978-3-030-30493-5_45&partnerID=40&md5=df9292b384c6581857ec0ea65277f7b1},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://link.springer.com/chapter/10.1007/978-3-030-30493-5_45},
}

ICANNC13

Playing the Large Margin Preference Game

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

In International Conference on Artificial Neural Networks and Machine Learning 2019

Bib HTML

@inproceedings{Polato2019icann,
  author = {Polato, M. and Faggioli, G. and Lauriola, I. and Aiolli, F.},
  title = {Playing the Large Margin Preference Game},
  booktitle = {International Conference on Artificial Neural Networks and Machine Learning},
  year = {2019},
  pages = {792-804},
  doi = {10.1007/978-3-030-30484-3_62},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072873359&doi=10.1007%2f978-3-030-30484-3_62&partnerID=40&md5=d974c2fb51e1cf5b445d7f3c3ad6b8a2},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://link.springer.com/chapter/10.1007/978-3-030-30484-3_62},
}

SACC12

Mind your wallet’s privacy: Identifying bitcoin wallet apps and user’s actions through network traffic analysis

Aiolli, F., Gangwal, A., Conti, M., and Polato, M.

In ACM Symposium on Applied Computing 2019

Bib

@inproceedings{Aiolli2019sac,
  author = {Aiolli, F. and Gangwal, A. and Conti, M. and Polato, M.},
  title = {Mind your wallet's privacy: Identifying bitcoin wallet apps and user's actions through network traffic analysis},
  booktitle = {ACM Symposium on Applied Computing},
  year = {2019},
  volume = {Part F147772},
  pages = {1484-1491},
  doi = {10.1145/3297280.3297430},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065675438&doi=10.1145%2f3297280.3297430&partnerID=40&md5=2a1e198c4041306bf96736783d5ef239},
  document_type = {Conference Paper},
  source = {Scopus},
}

DRNAJ05

Learning with subsampled kernel-based methods: Environmental and financial applications

Shahrokhabadi, M.A., Neisy, A., Perracchione, E., and Polato, M.

Dolomites Research Notes on Approximation 2019

Bib HTML

@article{Shahrokhabadi2019drna,
  author = {Shahrokhabadi, M.A. and Neisy, A. and Perracchione, E. and Polato, M.},
  title = {Learning with subsampled kernel-based methods: Environmental and financial applications},
  journal = {Dolomites Research Notes on Approximation},
  year = {2019},
  volume = {12},
  pages = {17-27},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064458005&partnerID=40&md5=ed1ef9463cf6d60248c95d798399a601},
  document_type = {Article},
  source = {Scopus},
  html = {https://www.emis.de/journals/DRNA/3-6.html},
}

2018

RECSYSW02

Efficient Similarity Based Methods For The Playlist Continuation Task

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

In ACM Recommender Systems Challenge 2018 2018

Bib HTML Code

@inproceedings{Faggioli2018recsys,
  author = {Faggioli, G. and Polato, M. and Aiolli, F.},
  title = {Efficient Similarity Based Methods For The Playlist Continuation Task},
  year = {2018},
  isbn = {9781450365864},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3267471.3267486},
  doi = {10.1145/3267471.3267486},
  booktitle = {ACM Recommender Systems Challenge 2018},
  articleno = {15},
  numpages = {6},
  keywords = {Playlist continuation, Top-N recommendation, Collaborative Filtering},
  location = {Vancouver, BC, Canada},
  series = {RecSys Challenge '18},
  html = {https://dl.acm.org/doi/10.1145/3267471.3267486},
  code = {https://github.com/guglielmof/recsys_spt2018},
}

COMPUTINGJ04

Time and activity sequence prediction of business process instances

Polato, M., Sperduti, A., Burattin, A., and Leoni, M.

Computing 2018

Bib Code

@article{Polato2018computing,
  author = {Polato, M. and Sperduti, A. and Burattin, A. and Leoni, M.},
  title = {Time and activity sequence prediction of business process instances},
  journal = {Computing},
  year = {2018},
  volume = {100},
  number = {9},
  pages = {1005-1031},
  doi = {10.1007/s00607-018-0593-x},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042069635&doi=10.1007%2fs00607-018-0593-x&partnerID=40&md5=5bbe3687beac8578f0540d744f8b7968},
  document_type = {Article},
  source = {Scopus},
  code = {https://www.dropbox.com/s/6ps93qmc81clz74/Polato_etal_Prediction_plugin_prom.zip},
}

ENTROPYJ03

A novel Boolean kernels family for categorical data

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

Entropy 2018

Bib HTML Code

@article{Polato2018entropy,
  author = {Polato, M. and Lauriola, I. and Aiolli, F.},
  title = {A novel Boolean kernels family for categorical data},
  journal = {Entropy},
  year = {2018},
  volume = {20},
  number = {6},
  doi = {10.3390/e20060444},
  art_number = {444},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048723680&doi=10.3390%2fe20060444&partnerID=40&md5=f121bfe1b4a0a6036e9633ad9fd9d1b3},
  document_type = {Article},
  source = {Scopus},
  code = {https://github.com/makgyver/pyros},
  html = {https://www.mdpi.com/1099-4300/20/6/444},
}

NEUCOMJ02

Boolean kernels for collaborative filtering in top-N item recommendation

Polato, M., and Aiolli, F.

Neurocomputing 2018

Bib HTML Code

@article{Polato2018neucom,
  author = {Polato, M. and Aiolli, F.},
  title = {Boolean kernels for collaborative filtering in top-N item recommendation},
  journal = {Neurocomputing},
  year = {2018},
  volume = {286},
  pages = {214-225},
  doi = {10.1016/j.neucom.2018.01.057},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042444689&doi=10.1016%2fj.neucom.2018.01.057&partnerID=40&md5=45f8270c852c88e4310c5b4a03723a09},
  document_type = {Article},
  source = {Scopus},
  code = {https://github.com/makgyver/pyros},
  html = {https://www.sciencedirect.com/science/article/pii/S0925231218300900},
}

SSCIC11

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

Navarin, N., Vincenzi, B., Polato, M., and Sperduti, A.

In IEEE Symposium Series on Computational Intelligence 2018

Bib HTML Code

@inproceedings{Navarin2018ssci,
  author = {Navarin, N. and Vincenzi, B. and Polato, M. and Sperduti, A.},
  title = {LSTM networks for data-aware remaining time prediction of business process instances},
  booktitle = {IEEE Symposium Series on Computational Intelligence},
  year = {2018},
  pages = {1-7},
  doi = {10.1109/SSCI.2017.8285184},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046127894&doi=10.1109%2fSSCI.2017.8285184&partnerID=40&md5=bc170feafff4cf30eb4437ab06ee4979},
  document_type = {Conference Paper},
  source = {Scopus},
  code = {https://github.com/nickgentoo/DALSTM_PM},
  html = {https://arxiv.org/pdf/1711.03822.pdf},
}

ESANNC10

Boolean kernels for interpretable kernel machines

Polato, M., and Aiolli, F.

In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2018

Bib HTML

@inproceedings{Polato2018esann,
  author = {Polato, M. and Aiolli, F.},
  title = {Boolean kernels for interpretable kernel machines},
  booktitle = {European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
  year = {2018},
  pages = {177-182},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061736966&partnerID=40&md5=bb91189f20ee4210483d5635ea42ad77},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://www.esann.org/sites/default/files/proceedings/legacy/es2018-168.pdf},
}

ESANNC09

The minimum effort maximum output principle applied to Multiple Kernel Learning

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

In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2018

Bib HTML

@inproceedings{Lauriola2018esann,
  author = {Lauriola, I. and Polato, M. and Aiolli, F.},
  title = {The minimum effort maximum output principle applied to Multiple Kernel Learning},
  booktitle = {European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
  year = {2018},
  pages = {183-188},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057816257&partnerID=40&md5=f474d89299973a8bf7e9b229a49da27f},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://www.esann.org/sites/default/files/proceedings/legacy/es2018-181.pdf},
}

ICANNC08

A game-theoretic framework for interpretable preference and feature learning

Polato, M., and Aiolli, F.

In International Conference on Artificial Neural Networks and Machine Learning 2018

Bib HTML

@inproceedings{Polato2018icann,
  author = {Polato, M. and Aiolli, F.},
  title = {A game-theoretic framework for interpretable preference and feature learning},
  booktitle = {International Conference on Artificial Neural Networks and Machine Learning},
  year = {2018},
  pages = {659-668},
  doi = {10.1007/978-3-030-01418-6_65},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054856770&doi=10.1007%2f978-3-030-01418-6_65&partnerID=40&md5=b592a2229ac368ab001e9b25fbbec5ad},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://link.springer.com/chapter/10.1007/978-3-030-01418-6_65},
}

ICANNC07

Learning preferences for large scale multi-label problems

Lauriola, I., Polato, M., Lavelli, A., Rinaldi, F., and Aiolli, F.

In International Conference on Artificial Neural Networks and Machine Learning 2018

Bib HTML

@inproceedings{Lauriola2018icann,
  author = {Lauriola, I. and Polato, M. and Lavelli, A. and Rinaldi, F. and Aiolli, F.},
  title = {Learning preferences for large scale multi-label problems},
  booktitle = {International Conference on Artificial Neural Networks and Machine Learning},
  year = {2018},
  pages = {546-555},
  doi = {10.1007/978-3-030-01418-6_54},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054802727&doi=10.1007%2f978-3-030-01418-6_54&partnerID=40&md5=379b27ec910d6bba5149a7eef29a8ccb},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://link.springer.com/chapter/10.1007/978-3-030-01418-6_54},
}

PhD Thesis

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

Polato, Mirko

2018

Bib HTML

@phdthesis{Polato2018thesis,
  place = {Padova},
  title = {Definition and learning of logic-based kernels for categorical data, and application to collaborative filtering},
  school = {University of Padova},
  author = {Polato, Mirko},
  year = {2018},
  pages = {1–162},
  html = {https://www.research.unipd.it/handle/11577/3427260?1/mirko_polato_tesi.pdf}
}

2017

NEUCOMJ01

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

Polato, M., and Aiolli, F.

Neurocomputing 2017

Bib HTML Code

@article{Polato2017neucom,
  author = {Polato, M. and Aiolli, F.},
  title = {Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation},
  journal = {Neurocomputing},
  year = {2017},
  volume = {268},
  pages = {17-26},
  doi = {10.1016/j.neucom.2016.12.090},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033375492&doi=10.1016%2fj.neucom.2016.12.090&partnerID=40&md5=d3c706a32c2407bf7418e2e30860a690},
  document_type = {Article},
  source = {Scopus},
  code = {https://github.com/makgyver/pyros},
  html = {https://www.sciencedirect.com/science/article/pii/S0925231217307592},
}

PRECEDEC06

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

Ru, D.D., Polato, M., and Bolognani, S.

In IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics 2017

Bib HTML

@inproceedings{Ru2017precede,
  author = {Ru, D.D. and Polato, M. and Bolognani, S.},
  title = {Model-free predictive current control for a SynRM drive based on an effective update of measured current responses},
  booktitle = {IEEE International Symposium on Predictive Control of Electrical Drives and Power Electronics},
  year = {2017},
  pages = {119-124},
  doi = {10.1109/PRECEDE.2017.8071279},
  art_number = {8071279},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039956942&doi=10.1109%2fPRECEDE.2017.8071279&partnerID=40&md5=c1281fc8cbaef5054f34c34817bf0586},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://ieeexplore.ieee.org/document/8071279},
}

ICANNC05

Classification of categorical data in the feature space of monotone DNFs

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

In International Conference on Artificial Neural Networks and Machine Learning 2017

Bib HTML

@inproceedings{Polato2017icann,
  author = {Polato, M. and Lauriola, I. and Aiolli, F.},
  title = {Classification of categorical data in the feature space of monotone DNFs},
  booktitle = {International Conference on Artificial Neural Networks and Machine Learning},
  year = {2017},
  pages = {279-286},
  doi = {10.1007/978-3-319-68612-7_32},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034258399&doi=10.1007%2f978-3-319-68612-7_32&partnerID=40&md5=2acaa87ee3c0e08c57c67edef5b10350},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://link.springer.com/chapter/10.1007/978-3-319-68612-7_32},
}

ICANNC04

Radius-margin ratio optimization for dot-product boolean kernel learning

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

In International Conference on Artificial Neural Networks and Machine Learning 2017

Bib HTML Code

@inproceedings{Lauriola2017icann,
  author = {Lauriola, I. and Polato, M. and Aiolli, F.},
  title = {Radius-margin ratio optimization for dot-product boolean kernel learning},
  booktitle = {International Conference on Artificial Neural Networks and Machine Learning},
  year = {2017},
  pages = {183-191},
  doi = {10.1007/978-3-319-68612-7_21},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034252146&doi=10.1007%2f978-3-319-68612-7_21&partnerID=40&md5=15b944c2fc6452153a3908aa324ecc75},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://link.springer.com/chapter/10.1007/978-3-319-68612-7_21},
  code = {https://github.com/IvanoLauriola/MKLpy},
}

IIRC03

Disjunctive Boolean Kernel based Collaborative Filtering for top-N item recommendation

Polato, M., and Aiolli, F.

In Italian Conference on Information Retrieval 2017

Bib HTML Code

@inproceedings{Polato2017iir,
  author = {Polato, M. and Aiolli, F.},
  title = {Disjunctive Boolean Kernel based Collaborative Filtering for top-N item recommendation},
  journal = {CEUR Workshop Proceedings},
  booktitle = {Italian Conference on Information Retrieval},
  year = {2017},
  volume = {1911},
  pages = {97-100},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029217075&partnerID=40&md5=b72a41ca678f66635ade365db8afe79b},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {http://ceur-ws.org/Vol-1911/17.pdf},
  code = {https://github.com/makgyver/pyros},
}

2016

RECSYSW01

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

Polato, M., and Aiolli, F.

In Recommender Systems Challenge 2016

Bib HTML

@inproceedings{Polato2016recsys,
  author = {Polato, M. and Aiolli, F.},
  title = {A preliminary study on a recommender system for the job recommendation challenge},
  booktitle = {Recommender Systems Challenge},
  year = {2016},
  doi = {10.1145/2987538.2987549},
  art_number = {a1},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014858005&doi=10.1145%2f2987538.2987549&partnerID=40&md5=1253e7c69995c73455efb09ab18cb195},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://dl.acm.org/doi/10.1145/2987538.2987549},
}

ESANNC02

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

Polato, M., and Aiolli, F.

In 24th European Symposium on Artificial Neural Networks 2016

Bib HTML Code

@inproceedings{Polato2016esann,
  author = {Polato, M. and Aiolli, F.},
  title = {Kernel based collaborative filtering for very large scale top-N item recommendation},
  booktitle = {24th European Symposium on Artificial Neural Networks},
  year = {2016},
  pages = {11-16},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994137519&partnerID=40&md5=e60e45267739305c485041cf1997d288},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://www.researchgate.net/publication/295080817_Kernel_based_collaborative_filtering_for_very_large_scale_top-N_item_recommendation},
  code = {https://github.com/makgyver/pyros},
}

2014

IJCNNC01

Data-aware remaining time prediction of business process instances

Polato, M., Sperduti, A., Burattin, A., and De Leoni, M.

In Proceedings of the International Joint Conference on Neural Networks 2014

Bib HTML Code

@inproceedings{Polato2014ijcnn,
  author = {Polato, M. and Sperduti, A. and Burattin, A. and De Leoni, M.},
  title = {Data-aware remaining time prediction of business process instances},
  booktitle = {Proceedings of the International Joint Conference on Neural Networks},
  year = {2014},
  pages = {816-823},
  doi = {10.1109/IJCNN.2014.6889360},
  art_number = {6889360},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908472113&doi=10.1109%2fIJCNN.2014.6889360&partnerID=40&md5=a385a0bf5eef92f8a7247e2f8f1d1a79},
  document_type = {Conference Paper},
  source = {Scopus},
  html = {https://ieeexplore.ieee.org/document/6889360},
  code = {https://www.dropbox.com/s/6ps93qmc81clz74/Polato_etal_Prediction_plugin_prom.zip},
}