Structured Machine Learning Group

Structured Machine Learning Group

Welcome to the website of the Structured Machine Learning Group at the University of Trento, Italy.

The SML group carries out research on different topics of theoretical and applied Machine Learning, with a focus on domains characterized by complex structures like sequences, trees, graphs and relational knowledge bases. Our main research activities involve combining statistical and symbolic approaches to learning, by integrating logic and constraint programming with statistical learning approaches. Active areas include structured-output prediction, learning to optimize, reasoning and learning in hybrid domains, constructive preference elicitation and bioinformatics applications. See here for more details on our research.


Recent Publications


SMT-based Weighted Model Integration with Structure Awareness Giuseppe Spallitta, Gabriele Masina, Paolo Morettin, Andrea Passerini, and Roberto Sebastiani. In The 38th Conference on Uncertainty in Artificial Intelligence, 2022.
@inproceedings {uai2022,
    author = { Spallitta, Giuseppe and Masina, Gabriele and Morettin, Paolo and Passerini, Andrea and Sebastiani, Roberto },
    title = "{SMT}-based Weighted Model Integration with Structure Awareness",
    booktitle = "The 38th Conference on Uncertainty in Artificial Intelligence",
    year = "2022",
    url = "papers/uai2022.pdf",
}
Skeptical Learning: An Algorithm and a Platform for Dealing with Mislabeling in Personal Context Recognition Wanyi Zhang, Mattia Zeni, Andrea Passerini, and Fausto Giunchiglia. In Algorithms 15(4), 2022.
@article {algo2022,
    author = { Zhang, Wanyi and Zeni, Mattia and Passerini, Andrea and Giunchiglia, Fausto },
    title = "Skeptical Learning: An Algorithm and a Platform for Dealing with Mislabeling in Personal Context Recognition",
    journal = "Algorithms",
    volume = "15",
    year = "2022",
    number = "4",
    article-number = "109",
    url = "papers/algo2022.pdf",
}
Human-in-the-loop handling of knowledge drift Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini, and Stefano Teso. In Data Mining and Knowledge Discovery, 2022.
@article {dami2022,
    author = { Bontempelli, Andrea and Giunchiglia, Fausto and Passerini, Andrea and Teso, Stefano },
    title = "Human-in-the-loop handling of knowledge drift",
    journal = "Data Mining and Knowledge Discovery",
    year = "2022",
    url = "papers/dami2022.pdf",
}
An efficient procedure for mining egocentric temporal motifs Antonio Longa, Giulia Cencetti, Bruno Lepri, and Andrea Passerini. In Data Mining and Knowledge Discovery, 2021.
@article {dami2021,
    author = { Longa, Antonio and Cencetti, Giulia and Lepri, Bruno and Passerini, Andrea },
    year = "2021",
    month = "11",
    title = "An efficient procedure for mining egocentric temporal motifs",
    journal = "Data Mining and Knowledge Discovery",
    doi = "10.1007/s10618-021-00803-2",
    url = "papers/dami2021.pdf",
    code = "https://github.com/AntonioLonga/Egocentric-Temporal-Motifs-Miner-ETMM",
}
Putting human behavior predictability in context Wanyi Zhang, Qiang Shen, Stefano Teso, Bruno Lepri, Andrea Passerini, Ivano Bison, and Fausto Giunchiglia. In {EPJ} Data Sci. 10(1), 2021.
@article {epj2021,
    author = { Zhang, Wanyi and Shen, Qiang and Teso, Stefano and Lepri, Bruno and Passerini, Andrea and Bison, Ivano and Giunchiglia, Fausto },
    title = "Putting human behavior predictability in context",
    journal = "{EPJ} Data Sci.",
    volume = "10",
    number = "1",
    pages = "42",
    year = "2021",
    url = "papers/epj2021.pdf",
}
The Science of Rejection: A Research Area for Human Computation Burcu Sayin, Jie Yang, Andrea Passerini, and Fabio Casati. In Proceedings of the 9th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2021), 2021.
@inproceedings {hcomp2021,
    author = { Sayin, Burcu and Yang, Jie and Passerini, Andrea and Casati, Fabio },
    title = "The Science of Rejection: {A} Research Area for Human Computation",
    booktitle = "Proceedings of the 9th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2021)",
    year = "2021",
    url = "papers/hcomp2021.pdf",
    note = "(best blue sky ideas paper award)",
}
Learning compositional programs with arguments and sampling Giovanni De Toni, Luca Erculiani, and Andrea Passerini. In Advances in Programming Languages and Neurosymbolic Systems (AIPLANS), NeurIPS, 2021.
@inproceedings {aiplans2021,
    author = { Toni, Giovanni De and Erculiani, Luca and Passerini, Andrea },
    title = "Learning compositional programs with arguments and sampling",
    booktitle = "Advances in Programming Languages and Neurosymbolic Systems (AIPLANS), NeurIPS",
    year = "2021",
    url = "papers/aiplans2021.pdf",
    code = "https://github.com/geektoni/learning_programs_with_arguments",
}
Learning compositional programs with arguments and sampling Giovanni De Toni, Luca Erculiani, and Andrea Passerini. In 10th International Workshop on Statistical Relational AI (StarAI), IJCLR, 2021.
@inproceedings {starai2021,
    author = { Toni, Giovanni De and Erculiani, Luca and Passerini, Andrea },
    title = "Learning compositional programs with arguments and sampling",
    booktitle = "10th International Workshop on Statistical Relational AI (StarAI), IJCLR",
    year = "2021",
    url = "papers/starai2021.pdf",
    code = "https://github.com/geektoni/learning_programs_with_arguments",
}
See complete publication list