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


Personalized Algorithmic Recourse with Preference Elicitation Giovanni De Toni, Paolo Viappiani, Stefano Teso, Bruno Lepri, and Andrea Passerini. In Transactions on Machine Learning Research, 2024.
@article {tmlr2024,
    author = { Toni, Giovanni De and Viappiani, Paolo and Teso, Stefano and Lepri, Bruno and Passerini, Andrea },
    title = "Personalized Algorithmic Recourse with Preference Elicitation",
    journal = "Transactions on Machine Learning Research",
    issn = "2835-8856",
    year = "2024",
    url = "https://openreview.net/forum?id=8sg2I9zXgO",
    note = "",
}
Enhancing SMT-based Weighted Model Integration by structure awareness Giuseppe Spallitta, Gabriele Masina, Paolo Morettin, Andrea Passerini, and Roberto Sebastiani. In Artificial Intelligence, 2024.
@article {aij2024,
    author = { Spallitta, Giuseppe and Masina, Gabriele and Morettin, Paolo and Passerini, Andrea and Sebastiani, Roberto },
    title = "Enhancing SMT-based Weighted Model Integration by structure awareness",
    journal = "Artificial Intelligence",
    volume = "328",
    pages = "104067",
    year = "2024",
    issn = "0004-3702",
    doi = "https://doi.org/10.1016/j.artint.2024.104067",
    url = "https://www.sciencedirect.com/science/article/pii/S0004370224000031",
    keywords = "Hybrid probabilistic inference, Weighted Model Integration, Satisfiability modulo theories",
}
Generating fine-grained surrogate temporal networks Antonio Longa, Giulia Cencetti, Sune Lehmann, Andrea Passerini, and Bruno Lepri. In Communications Physics 7(22), 2024.
@article {commphys2024,
    author = { Longa, Antonio and Cencetti, Giulia and Lehmann, Sune and Passerini, Andrea and Lepri, Bruno },
    title = "Generating fine-grained surrogate temporal networks",
    year = "2024",
    journal = "Communications Physics",
    volume = "7",
    number = "22",
    url = "https://arxiv.org/abs/2205.08820",
}
Environmentally-Aware Bundle Recommendation Using the Choquet Integral Marco Bronzini, Erich Robbi, Paolo Viappiani, and Andrea Passerini. In {12th Conference on Prestigious Applications of Intelligent Systems (PAIS 2023)} (Frontiers in Artificial Intelligence and Applications), 2023.
@inproceedings {pais2023choquet,
    author = { Bronzini, Marco and Robbi, Erich and Viappiani, Paolo and Passerini, Andrea },
    title = "{Environmentally-Aware Bundle Recommendation Using the Choquet Integral}",
    booktitle = "{12th Conference on Prestigious Applications of Intelligent Systems (PAIS 2023)}",
    address = "Krakow, Poland, Poland",
    publisher = "{IOS Press}",
    series = "Frontiers in Artificial Intelligence and Applications",
    number = "372",
    pages = "3182--3189",
    year = "2023",
    month = "September",
    doi = "10.3233/FAIA230639",
    url = "https://hal.science/hal-04292392/file/FAIA-372-FAIA230639.pdf",
}
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis Giovanni De Toni, Bruno Lepri, and Andrea Passerini. In Mach. Learn. 112(4), 2023.
@article {mach2023,
    author = { De Toni, Giovanni and Lepri, Bruno and Passerini, Andrea },
    title = "Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis",
    year = "2023",
    issue_date = "Apr 2023",
    publisher = "Kluwer Academic Publishers",
    address = "USA",
    volume = "112",
    number = "4",
    issn = "0885-6125",
    url = "https://doi.org/10.1007/s10994-022-06293-7",
    doi = "10.1007/s10994-022-06293-7",
    journal = "Mach. Learn.",
    month = "feb",
    pages = "1389–1409",
    numpages = "21",
    keywords = "Machine learning, Explainable AI, Counterfactuals examples, Algorithmic recourse",
}
Semantic Loss Functions for Neuro-Symbolic Structured Prediction Kareem Ahmed, Stefano Teso, Paolo Morettin, Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Yitao Liang, Eric Wang, Kai{-}Wei Chang, Andrea Passerini, and Guy Van Broeck. In Unknown venue (type=incollection), 2023.
@incollection {faia2023,
    author = { Ahmed, Kareem and Teso, Stefano and Morettin, Paolo and Liello, Luca Di and Ardino, Pierfrancesco and Gobbi, Jacopo and Liang, Yitao and Wang, Eric and Chang, Kai{-}Wei and Passerini, Andrea and den Broeck, Guy Van },
    title = "Semantic Loss Functions for Neuro-Symbolic Structured Prediction",
    booktitle = "Compendium of Neurosymbolic Artificial Intelligence",
    series = "Frontiers in Artificial Intelligence and Applications",
    volume = "369",
    pages = "485--505",
    publisher = "{IOS} Press",
    year = "2023",
    url = "https://doi.org/10.3233/FAIA230154",
    doi = "10.3233/FAIA230154",
}
Adaptation of Student Behavioural Routines during COVID-19: A Multimodal Approach Nicolò A. Girardini, Simone Centellegher, Andrea Passerini, Ivano Bison, Fausto Giunchiglia, and Bruno Lepri. In EPJ Data Science 12(55), 2023.
@article {epjdatascience2023,
    author = { Girardini, Nicolò A. and Centellegher, Simone and Passerini, Andrea and Bison, Ivano and Giunchiglia, Fausto and Lepri, Bruno },
    title = "Adaptation of Student Behavioural Routines during COVID-19: A Multimodal Approach",
    year = "2023",
    journal = "EPJ Data Science",
    volume = "12",
    number = "55",
    url = "papers/epjdatascience2023.pdf",
}
Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning Emanuele Marconato, Andrea Passerini, and Stefano Teso. In Entropy 25(12), 2023.
@article {entropy2023,
    author = { Marconato, Emanuele and Passerini, Andrea and Teso, Stefano },
    title = "Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning",
    journal = "Entropy",
    volume = "25",
    year = "2023",
    number = "12",
    article-number = "1574",
    url = "https://www.mdpi.com/1099-4300/25/12/1574",
    doi = "10.3390/e25121574",
}
See complete publication list