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2021

An efficient procedure for mining egocentric temporal motifs Antonio Longa, Giulia Cencetti, Bruno Lepri, and Andrea Passerini. In Data Mining and Knowledge Discovery.
@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).
@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).
@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.
@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.
@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",
}
Neuro-Symbolic Constraint Programming for Structured Prediction Paolo Dragone, Stefano Teso, and Andrea Passerini. In Proceedings of the 15th International Workshop on Neural-Symbolic Learning and Reasoning as part of the 1st International Joint Conference on Learning {\&} Reasoning {(IJCLR} 2021).
@inproceedings {nesy2021,
    author = { Dragone, Paolo and Teso, Stefano and Passerini, Andrea },
    title = "Neuro-Symbolic Constraint Programming for Structured Prediction",
    booktitle = "Proceedings of the 15th International Workshop on Neural-Symbolic Learning and Reasoning as part of the 1st International Joint Conference on Learning {\&} Reasoning {(IJCLR} 2021)",
    volume = "2986",
    pages = "6--14",
    year = "2021",
    url = "http://ceur-ws.org/Vol-2986/paper2.pdf",
}
Interactive Label Cleaning with Example-based Explanations Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia, and Andrea Passerini. In Proceedings of NeurIPS.
@inproceedings {neurips2021,
    author = { Teso, Stefano and Bontempelli, Andrea and Giunchiglia, Fausto and Passerini, Andrea },
    title = "Interactive Label Cleaning with Example-based Explanations",
    booktitle = "Proceedings of NeurIPS",
    year = "2021",
    url = "papers/neurips2021.pdf",
}
The misunderstanding of vaccine efficacy K. Tentori, A. Passerini, B. Timberlake, and S. Pighin. In Social Science and Medicine.
@article {ssm2021,
    author = { Tentori, K. and Passerini, A. and Timberlake, B. and Pighin, S. },
    title = "The misunderstanding of vaccine efficacy",
    journal = "Social Science and Medicine",
    volume = "289",
    pages = "114273",
    year = "2021",
    issn = "0277-9536",
    doi = "https://doi.org/10.1016/j.socscimed.2021.114273",
    url = "https://www.sciencedirect.com/science/article/pii/S0277953621006055",
    keywords = "Vaccine efficacy, Risk communication, SARS-CoV-2 vaccine, Covid-19",
}
A Neuro-Symbolic Approach to Structured Event Recognition Gianluca Apriceno, Andrea Passerini, and Luciano Serafini. In 28th International Symposium on Temporal Representation and Reasoning (TIME 2021).
@inproceedings {time2021,
    author = { Apriceno, Gianluca and Passerini, Andrea and Serafini, Luciano },
    title = "{A Neuro-Symbolic Approach to Structured Event Recognition}",
    booktitle = "28th International Symposium on Temporal Representation and Reasoning (TIME 2021)",
    pages = "11:1--11:14",
    year = "2021",
    volume = "206",
    doi = "10.4230/LIPIcs.TIME.2021.11",
    url = "papers/time2021.pdf",
}
Is Parameter Learning via Weighted Model Integration Tractable? Zhe Zeng, Paolo Morettin, Fanqi Yan, Andrea Passerini, and Guy Van Broeck. In The 4th Workshop on Tractable Probabilistic Modeling.
@inproceedings {tpm2021,
    author = { Zeng, Zhe and Morettin, Paolo and Yan, Fanqi and Passerini, Andrea and den Broeck, Guy Van },
    title = "Is Parameter Learning via Weighted Model Integration Tractable?",
    booktitle = "The 4th Workshop on Tractable Probabilistic Modeling",
    year = "2021",
    url = "papers/tpm2021.pdf",
}
Towards Visual Semantics F. Giunchiglia, L. Erculiani, and A. Passerini. In SN COMPUT. SCI. 2(446).
@article {sncs2021,
    author = { Giunchiglia, F. and Erculiani, L. and Passerini, A. },
    title = "Towards Visual Semantics",
    journal = "SN COMPUT. SCI.",
    year = "2021",
    volume = "2",
    number = "446",
    doi = "https://doi.org/10.1007/s42979-021-00839-7",
    url = "papers/sncs2021.pdf",
}
Co-creating Platformer Levels with Constrained Adversarial Networks Paolo Morettin, Andrea Passerini, and Stefano Teso. In Proceedings of the 2nd Workshop on Human-AI Co-Creation with Generative Models.
@inproceedings {hai-gen2021,
    author = { Morettin, Paolo and Passerini, Andrea and Teso, Stefano },
    booktitle = "Proceedings of the 2nd Workshop on Human-AI Co-Creation with Generative Models",
    year = "2021",
    title = "Co-creating Platformer Levels with Constrained Adversarial Networks",
    url = "papers/hai-gen2021.pdf",
}
A review and experimental analysis of active learningover crowdsourced data Burcu Sayin, Evgeny Krivosheev, Jie Yang, Andrea Passerini, and Fabio Casati. In Artificial Intelligence Review.
@article {air2021,
    author = { Sayin, Burcu and Krivosheev, Evgeny and Yang, Jie and Passerini, Andrea and Casati, Fabio },
    title = "A review and experimental analysis of active learningover crowdsourced data",
    journal = "Artificial Intelligence Review",
    year = "2021",
    url = "papers/air2021.pdf",
}
Hybrid probabilistic inference with logical and algebraic constraints: a survey Paolo Morettin, Pedro Zuidberg Dos Martires, Samuel Kolb, and Andrea Passerini. In Proceedings of the 30th International Joint Conference on Artificial Intelligence.
@inproceedings {ijcai_surv2021,
    author = { Morettin, Paolo and Zuidberg Dos Martires, Pedro and Kolb, Samuel and Passerini, Andrea },
    booktitle = "Proceedings of the 30th International Joint Conference on Artificial Intelligence",
    year = "2021",
    title = "Hybrid probabilistic inference with logical and algebraic constraints: a survey",
    url = "papers/ijcai_surv2021.pdf",
}
Learning Aggregation Functions Giovanni Pellegrini, Alessandro Tibo, Paolo Frasconi, Andrea Passerini, and Manfred Jaeger. In Proceedings of the 30th International Joint Conference on Artificial Intelligence.
@inproceedings {ijcai2021,
    author = { Pellegrini, Giovanni and Tibo, Alessandro and Frasconi, Paolo and Passerini, Andrea and Jaeger, Manfred },
    booktitle = "Proceedings of the 30th International Joint Conference on Artificial Intelligence",
    year = "2021",
    title = "Learning Aggregation Functions",
    code = "https://github.com/alessandro-t/laf",
    url = "papers/ijcai2021.pdf",
}
Learning Modulo Theories for constructive preference elicitation Paolo Campigotto, Stefano Teso, Roberto Battiti, and Andrea Passerini. In Artificial Intelligence.
@article {aij2021,
    author = { Campigotto, Paolo and Teso, Stefano and Battiti, Roberto and Passerini, Andrea },
    title = "Learning Modulo Theories for constructive preference elicitation",
    journal = "Artificial Intelligence",
    volume = "295",
    pages = "103454",
    year = "2021",
    issn = "0004-3702",
    doi = "https://doi.org/10.1016/j.artint.2021.103454",
    url = "papers/aij2021.pdf",
    keywords = "Preference elicitation, Learning while optimizing, (Maximum) Satisfiability Modulo Theory, Constructive machine learning",
}
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment Mirco Nanni, Gennady L. Andrienko, Albert{-}L{\'{a}}szl{\'{o}} Barab{\'{a}}si, Chiara Boldrini, Francesco Bonchi, Ciro Cattuto, Francesca Chiaromonte, Giovanni Comand{\'{e}}, Marco Conti, Mark Cot{\'{e}}, Frank Dignum, Virginia Dignum, Josep Domingo{-}Ferrer, Paolo Ferragina, Fosca Giannotti, Riccardo Guidotti, Dirk Helbing, Kimmo Kaski, J{\'{a}}nos Kert{\'{e}}sz, Sune Lehmann, Bruno Lepri, Paul Lukowicz, Stan Matwin, David Meg{\'{\i}}as, Anna Monreale, Katharina Morik, Nuria Oliver, Andrea Passarella, Andrea Passerini, Dino Pedreschi, Alex Pentland, Fabio Pianesi, Francesca Pratesi, Salvatore Rinzivillo, Salvatore Ruggieri, Arno Siebes, Vicen{\c{c}} Torra, Roberto Trasarti, Jeroen Hoven, and Alessandro Vespignani. In Ethics and Information Technology.
@article {eit2021,
    author = { Nanni, Mirco and Andrienko, Gennady L. and Barab{\'{a}}si, Albert{-}L{\'{a}}szl{\'{o}} and Boldrini, Chiara and Bonchi, Francesco and Cattuto, Ciro and Chiaromonte, Francesca and Comand{\'{e}}, Giovanni and Conti, Marco and Cot{\'{e}}, Mark and Dignum, Frank and Dignum, Virginia and Domingo{-}Ferrer, Josep and Ferragina, Paolo and Giannotti, Fosca and Guidotti, Riccardo and Helbing, Dirk and Kaski, Kimmo and Kert{\'{e}}sz, J{\'{a}}nos and Lehmann, Sune and Lepri, Bruno and Lukowicz, Paul and Matwin, Stan and Meg{\'{\i}}as, David and Monreale, Anna and Morik, Katharina and Oliver, Nuria and Passarella, Andrea and Passerini, Andrea and Pedreschi, Dino and Pentland, Alex and Pianesi, Fabio and Pratesi, Francesca and Rinzivillo, Salvatore and Ruggieri, Salvatore and Siebes, Arno and Torra, Vicen{\c{c}} and Trasarti, Roberto and van den Hoven, Jeroen and Vespignani, Alessandro },
    title = "Give more data, awareness and control to individual citizens, and they will help COVID-19 containment",
    journal = "Ethics and Information Technology",
    pages = "1--6",
    year = "2021",
    url = "https://link.springer.com/article/10.1007/s10676-020-09572-w",
}
2020

Dealing with Mislabeling via Interactive Machine Learning Wanyi Zhang, Andrea Passerini, and Fausto Giunchiglia. In KI - K{\"u}nstliche Intelligenz 34(2).
@article {Zhang2020,
    author = { Zhang, Wanyi and Passerini, Andrea and Giunchiglia, Fausto },
    title = "Dealing with Mislabeling via Interactive Machine Learning",
    journal = "KI - K{\"u}nstliche Intelligenz",
    year = "2020",
    month = "Jun",
    day = "01",
    volume = "34",
    number = "2",
    pages = "271-278",
    url = "papers/kunstint2020.pdf",
}
Efficient Generation of Structured Objects with Constrained Adversarial Networks Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, and Andrea Passerini. In Advances in Neural Information Processing Systems.
@article {neurips2020,
    author = { Di Liello, Luca and Ardino, Pierfrancesco and Gobbi, Jacopo and Morettin, Paolo and Teso, Stefano and Passerini, Andrea },
    title = "Efficient Generation of Structured Objects with Constrained Adversarial Networks",
    journal = "Advances in Neural Information Processing Systems",
    volume = "33",
    year = "2020",
    url = "papers/neurips20.pdf",
    code = "https://github.com/unitn-sml/CAN",
}
Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound S. Roy, W. Menapace, S. Oei, B. Luijten, E. Fini, C. Saltori, I. Huijben, N. Chennakeshava, F. Mento, A. Sentelli, E. Peschiera, R. Trevisan, G. Maschietto, E. Torri, R. Inchingolo, A. Smargiassi, G. Soldati, P. Rota, A. Passerini, R. J. G. Van Sloun, E. Ricci, and L. Demi. In IEEE Transactions on Medical Imaging.
@article {tmi2020,
    author = { Roy, S. and Menapace, W. and Oei, S. and Luijten, B. and Fini, E. and Saltori, C. and Huijben, I. and Chennakeshava, N. and Mento, F. and Sentelli, A. and Peschiera, E. and Trevisan, R. and Maschietto, G. and Torri, E. and Inchingolo, R. and Smargiassi, A. and Soldati, G. and Rota, P. and Passerini, A. and Sloun, R. J. G. Van and Ricci, E. and Demi, L. },
    journal = "IEEE Transactions on Medical Imaging",
    title = "Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound",
    year = "2020",
    volume = "",
    number = "",
    url = "papers/tmi2020.pdf",
    code = "https://github.com/mhug-Trento/DL4covidUltrasound",
}
Learning in the Wild with Incremental Skeptical Gaussian Processes A. Bontempelli, S. Teso, F. Giunchiglia, and A. Passerini. In Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20).
@inproceedings {ijcai20,
    author = { Bontempelli, A. and Teso, S. and Giunchiglia, F. and Passerini, A. },
    title = "Learning in the Wild with Incremental Skeptical Gaussian Processes",
    booktitle = "Proceedings of the 29th International Joint Conference on Artificial Intelligence",
    series = "IJCAI'20",
    year = "2020",
    notes = "accepted",
    url = "papers/ijcai20.pdf",
    code = "https://gitlab.com/abonte/incremental-skeptical-gp",
}
Learning Weighted Model Integration Distributions Paolo Morettin, Samuel Kolb, Stefano Teso, and Andrea Passerini. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI).
@inproceedings {aaai2020,
    author = { Morettin, Paolo and Kolb, Samuel and Teso, Stefano and Passerini, Andrea },
    booktitle = "Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI)",
    year = "2020",
    title = "Learning Weighted Model Integration Distributions",
    url = "papers/aaai20.pdf",
    code = "https://github.com/weighted-model-integration/LARIAT",
}
Continual egocentric object recognition L. Erculiani, F. Giunchiglia, and A. Passerini. In ECAI.
@article {ecai20,
    author = { Erculiani, L. and Giunchiglia, F. and Passerini, A. },
    title = "Continual egocentric object recognition",
    year = "2020",
    journal = "ECAI",
    notes = "accepted",
    url = "https://arxiv.org/pdf/1912.05029",
    code = "https://github.com/lucaerculiani/ecai20-continual-egocentric-object-recognition",
}
2019

Fixing Mislabeling by Human Annotators Leveraging Conflict Resolution and Prior Knowledge Mattia Zeni, Wanyi Zhang, Enrico Bignotti, Andrea Passerini, and Fausto Giunchiglia. In Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(1).
@article {ubiq19,
    author = { Zeni, Mattia and Zhang, Wanyi and Bignotti, Enrico and Passerini, Andrea and Giunchiglia, Fausto },
    title = "Fixing Mislabeling by Human Annotators Leveraging Conflict Resolution and Prior Knowledge",
    journal = "Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.",
    issue_date = "March 2019",
    volume = "3",
    number = "1",
    month = "March",
    year = "2019",
    issn = "2474-9567",
    pages = "32:1--32:23",
    articleno = "32",
    numpages = "23",
    url = "papers/ubicomp19.pdf",
    doi = "10.1145/3314419",
    acmid = "3314419",
    publisher = "ACM",
    address = "New York, NY, USA",
    keywords = "Annotation Errors, Collaborative and Social Computing, Ubiquitous and Mobile Devices",
}
Counts-of-counts similarity for prediction and search in relational data Manfred Jaeger, Marco Lippi, Giovanni Pellegrini, and Andrea Passerini. In Data Mining and Knowledge Discovery.
@article {dmkd19,
    author = { Jaeger, Manfred and Lippi, Marco and Pellegrini, Giovanni and Passerini, Andrea },
    year = "2019",
    month = "03",
    pages = "",
    title = "Counts-of-counts similarity for prediction and search in relational data",
    journal = "Data Mining and Knowledge Discovery",
    doi = "10.1007/s10618-019-00621-7",
    url = "papers/dmkd19.pdf",
}
The Pywmi Framework and Toolbox for Probabilistic Inference Using Weighted Model Integration Samuel Kolb, Paolo Morettin, Pedro Zuidberg Dos Martires, Francesco Sommavilla, Andrea Passerini, Roberto Sebastiani, and Luc De Raedt. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19).
@inproceedings {ijcai19,
    author = { Kolb, Samuel and Morettin, Paolo and Martires, Pedro Zuidberg Dos and Sommavilla, Francesco and Passerini, Andrea and Sebastiani, Roberto and De Raedt, Luc },
    title = "The Pywmi Framework and Toolbox for Probabilistic Inference Using Weighted Model Integration",
    booktitle = "Proceedings of the 28th International Joint Conference on Artificial Intelligence",
    series = "IJCAI'19",
    year = "2019",
    isbn = "978-0-9992411-4-1",
    location = "Macao, China",
    pages = "6530--6532",
    numpages = "3",
    url = "papers/ijcai19.pdf",
    acmid = "3368003",
    publisher = "AAAI Press",
    code = "https://github.com/weighted-model-integration/pywmi",
}
Advanced SMT techniques for weighted model integration Paolo Morettin, Andrea Passerini, and Roberto Sebastiani. In Artificial Intelligence.
@article {aij19,
    author = { Morettin, Paolo and Passerini, Andrea and Sebastiani, Roberto },
    title = "Advanced SMT techniques for weighted model integration",
    journal = "Artificial Intelligence",
    volume = "275",
    pages = "1 - 27",
    year = "2019",
    issn = "0004-3702",
    doi = "https://doi.org/10.1016/j.artint.2019.04.003",
    url = "papers/aij19.pdf",
    code = "https://github.com/unitn-sml/wmi-pa",
}
A Big Data and machine learning approach for network monitoring and security Leonardo Maccari and Andrea Passerini. In Security and Privacy 2(1).
@article {spy19,
    author = { Maccari, Leonardo and Passerini, Andrea },
    title = "A Big Data and machine learning approach for network monitoring and security",
    journal = "Security and Privacy",
    volume = "2",
    number = "1",
    pages = "e53",
    keywords = "big data, machine learning, mesh networks, network monitoring, root cause analysis",
    doi = "10.1002/spy2.53",
    year = "2019",
    url = "papers/sp19.pdf",
}
Combining Learning and Constraints for Genome-wide Protein Annotation Stefano Teso, Luca Masera, Michelangelo Diligenti, and Andrea Passerini. In BMC-Bioinformatics 20(338).
@article {bmc19,
    author = { Teso, Stefano and Masera, Luca and Diligenti, Michelangelo and Passerini, Andrea },
    title = "Combining Learning and Constraints for Genome-wide Protein Annotation",
    journal = "BMC-Bioinformatics",
    year = "2019",
    volume = "20",
    url = "papers/bmc19.pdf",
    number = "338",
}
2018

Learning SMT(LRA) Constraints using SMT Solvers Samuel Kolb, Stefano Teso, Andrea Passerini, and Luc De Raedt. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, {IJCAI-18}.
@inproceedings {ijcai2018smtle,
    author = { Kolb, Samuel and Teso, Stefano and Passerini, Andrea and Raedt, Luc De },
    title = "Learning SMT(LRA) Constraints using SMT Solvers",
    booktitle = "Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, {IJCAI-18}",
    publisher = "International Joint Conferences on Artificial Intelligence Organization",
    pages = "2333--2340",
    year = "2018",
    month = "7",
    doi = "10.24963/ijcai.2018/323",
    url = "https://doi.org/10.24963/ijcai.2018/323",
    code = "https://github.com/smtlearning/incal",
}
Pyconstruct: Constraint Programming Meets Structured Prediction Paolo Dragone, Stefano Teso, and Andrea Passerini. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, {IJCAI-18}.
@inproceedings {ijcai2018pyco,
    author = { Dragone, Paolo and Teso, Stefano and Passerini, Andrea },
    title = "Pyconstruct: Constraint Programming Meets Structured Prediction",
    booktitle = "Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, {IJCAI-18}",
    publisher = "International Joint Conferences on Artificial Intelligence Organization",
    pages = "5823--5825",
    year = "2018",
    month = "7",
    doi = "10.24963/ijcai.2018/850",
    url = "https://doi.org/10.24963/ijcai.2018/850",
    code = "https://github.com/unitn-sml/pyconstruct",
}
Constructive Preference Elicitation Paolo Dragone, Stefano Teso, and Andrea Passerini. In Frontiers in Robotics and AI.
@article {frontiers_robotics_ai_2018,
    author = { Dragone, Paolo and Teso, Stefano and Passerini, Andrea },
    title = "Constructive Preference Elicitation",
    journal = "Frontiers in Robotics and AI",
    volume = "4",
    pages = "71",
    year = "2018",
    url = "https://www.frontiersin.org/article/10.3389/frobt.2017.00071",
    doi = "10.3389/frobt.2017.00071",
    issn = "2296-9144",
}
Learning Constraints from Examples Luc De Raedt, Andrea Passerini, and Stefano Teso. In Proceedings of the 32nd Conference on Artificial Intelligence (AAAI).
@inproceedings {aaai18_cl,
    author = { Raedt, Luc De and Passerini, Andrea and Teso, Stefano },
    title = "Learning Constraints from Examples",
    booktitle = "Proceedings of the 32nd Conference on Artificial Intelligence (AAAI)",
    url = "papers/aaai18_cl.pdf",
    year = "2018",
}
Decomposition Strategies for Constructive Preference Elicitation Paolo Dragone, Stefano Teso, and Andrea Passerini. In Proceedings of the 32nd Conference on Artificial Intelligence (AAAI).
@inproceedings {aaai18_sketch,
    author = { Dragone, Paolo and Teso, Stefano and Passerini, Andrea },
    title = "Decomposition Strategies for Constructive Preference Elicitation",
    booktitle = "Proceedings of the 32nd Conference on Artificial Intelligence (AAAI)",
    url = "papers/aaai18_sketch.pdf",
    year = "2018",
    code = "https://github.com/unitn-sml/pcl",
}
Constructive Preference Elicitation over Hybrid Combinatorial Spaces Paolo Dragone, Stefano Teso, and Andrea Passerini. In Proceedings of the 32nd Conference on Artificial Intelligence (AAAI).
@inproceedings {aaai18_store,
    author = { Dragone, Paolo and Teso, Stefano and Passerini, Andrea },
    title = "Constructive Preference Elicitation over Hybrid Combinatorial Spaces",
    booktitle = "Proceedings of the 32nd Conference on Artificial Intelligence (AAAI)",
    url = "papers/aaai18_store.pdf",
    year = "2018",
    code = "https://github.com/unitn-sml/choice-perceptron",
}
Automating Layout Synthesis with Constructive Preference Elicitation Luca Erculiani, Paolo Dragone, Stefano Teso, and Andrea Passerini. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2018).
@inproceedings {ecmlpkdd18,
    author = { Erculiani, Luca and Dragone, Paolo and Teso, Stefano and Passerini, Andrea },
    title = "Automating Layout Synthesis with Constructive Preference Elicitation",
    booktitle = "Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2018)",
    year = "2018",
    url = "papers/ecml2018.pdf",
    code = "https://github.com/unitn-sml/constructive-layout-synthesis/tree/master/ecml18",
}
No More Ready-made Deals: Constructive Recommendation for Telco Service Bundling Paolo Dragone, Pellegrini Giovanni, Michele Vescovi, Katya Tentori, and Andrea Passerini. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys 2018).
@inproceedings {recsys18,
    author = { Dragone, Paolo and Giovanni, Pellegrini and Vescovi, Michele and Tentori, Katya and Passerini, Andrea },
    title = "No More Ready-made Deals: Constructive Recommendation for Telco Service Bundling",
    booktitle = "Proceedings of the 12th ACM Conference on Recommender Systems (RecSys 2018)",
    year = "2018",
    url = "papers/recsys2018.pdf",
}
2017

Investigating the association between social interactions and personality states dynamics Didem Gundogdu, Ailbhe N Finnerty, Jacopo Staiano, Stefano Teso, Andrea Passerini, Fabio Pianesi, and Bruno Lepri. In R Soc Open Sci 4(9).
@article {rs_openscience17,
    author = { Gundogdu, Didem and Finnerty, Ailbhe N and Staiano, Jacopo and Teso, Stefano and Passerini, Andrea and Pianesi, Fabio and Lepri, Bruno },
    date-added = "2018-01-09 10:19:05 +0000",
    date-modified = "2018-01-09 10:19:05 +0000",
    doi = "10.1098/rsos.170194",
    journal = "R Soc Open Sci",
    journal-full = "Royal Society open science",
    keywords = "ego-centric graphlets; experience-sampling method; linear mixed models; personality states; social interactions; wearable sensing",
    month = "Sep",
    number = "9",
    pages = "170194",
    pmc = "PMC5627072",
    pmid = "28989732",
    pst = "epublish",
    title = "Investigating the association between social interactions and personality states dynamics",
    volume = "4",
    year = "2017",
    bdsk-url-1 = "https://dx.doi.org/10.1098/rsos.170194",
}
Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects Seyed Mostafa Kia, Sandro Vega Pons, Nathan Weisz, and Andrea Passerini. In Frontiers in Neuroscience.
@article {fnins17,
    author = { Kia, Seyed Mostafa and Vega Pons, Sandro and Weisz, Nathan and Passerini, Andrea },
    title = "Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects",
    journal = "Frontiers in Neuroscience",
    volume = "10",
    pages = "619",
    year = "2017",
    url = "http://journal.frontiersin.org/article/10.3389/fnins.2016.00619",
    doi = "10.3389/fnins.2016.00619",
}
Coactive Critiquing: Elicitation of Preferences and Features Stefano Teso, Paolo Dragone, and Andrea Passerini. In Proceedings of the 31st Conference on Artificial Intelligence (AAAI).
@inproceedings {aaai17,
    author = { Teso, Stefano and Dragone, Paolo and Passerini, Andrea },
    title = "Coactive Critiquing: Elicitation of Preferences and Features",
    booktitle = "Proceedings of the 31st Conference on Artificial Intelligence (AAAI)",
    url = "papers/aaai2017.pdf",
    year = "2017",
}
Structured learning modulo theories Stefano Teso, Roberto Sebastiani, and Andrea Passerini. In Artificial Intelligence.
@article {TESO2017166,
    author = { Teso, Stefano and Sebastiani, Roberto and Passerini, Andrea },
    title = "Structured learning modulo theories",
    journal = "Artificial Intelligence",
    volume = "244",
    pages = "166 - 187",
    year = "2017",
    note = "Combining Constraint Solving with Mining and Learning",
    issn = "0004-3702",
    doi = "https://doi.org/10.1016/j.artint.2015.04.002",
    keywords = "Satisfiability modulo theory, Structured-output support vector machines, Optimization modulo theory, Constructive machine learning, Learning with constraints",
    url = "papers/aij2015.pdf",
}
Efficient Weighted Model Integration via SMT-Based Predicate Abstraction Paolo Morettin, Andrea Passerini, and Roberto Sebastiani. In Proc. Int. Joint Conference on Artificial Intelligence (IJCAI).
@inproceedings {ijcai17,
    author = { Morettin, Paolo and Passerini, Andrea and Sebastiani, Roberto },
    title = "Efficient Weighted Model Integration via SMT-Based Predicate Abstraction",
    booktitle = "Proc. Int. Joint Conference on Artificial Intelligence (IJCAI)",
    year = "2017",
    url = "papers/ijcai17.pdf",
}
Constructive Preference Elicitation for Multiple Users with Setwise Maxmargin Stefano Teso, Andrea Passerini, and Paolo Viappian. In Proc. International Conference on Algorithmic Decision Theory (ADT).
@inproceedings {adt17,
    author = { Teso, Stefano and Passerini, Andrea and Viappian, Paolo },
    title = "Constructive Preference Elicitation for Multiple Users with Setwise Maxmargin",
    booktitle = "Proc. International Conference on Algorithmic Decision Theory (ADT)",
    year = "2017",
    url = "papers/adt17.pdf",
}
Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning Seyed Mostafa Kia, Fabian Pedregosa, Anna Blumenthal, and Andrea Passerini. In Journal of Neuroscience Methods.
@article {Kia201797,
    author = { Kia, Seyed Mostafa and Pedregosa, Fabian and Blumenthal, Anna and Passerini, Andrea },
    title = "Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning",
    journal = "Journal of Neuroscience Methods",
    volume = "285",
    number = "",
    pages = "97 - 108",
    year = "2017",
    note = "",
    issn = "0165-0270",
    doi = "https://doi.org/10.1016/j.jneumeth.2017.05.004",
    url = "http://www.sciencedirect.com/science/article/pii/S0165027017301231",
    keywords = "MEG",
}
Introduction to the special issue on Combining Constraint Solving with Mining and Learning Andrea Passerini, Guido Tack, and Tias Guns. In Artificial Intelligence.
@article {PASSERINI20171,
    author = { Passerini, Andrea and Tack, Guido and Guns, Tias },
    title = "Introduction to the special issue on Combining Constraint Solving with Mining and Learning",
    journal = "Artificial Intelligence",
    volume = "244",
    pages = "1 - 5",
    year = "2017",
    note = "Combining Constraint Solving with Mining and Learning",
    issn = "0004-3702",
    doi = "https://doi.org/10.1016/j.artint.2017.01.002",
}
2016

ECML PKDD 2016 Journal Track Special Issue Thomas Gaertner, Mirco Nanni, Andrea Passerini, and Celine Robardet. In Data Mining and Knowledge Discovery 30(5).
@article {eclmpkdd_dmkd16,
    author = { Gaertner, Thomas and Nanni, Mirco and Passerini, Andrea and Robardet, Celine },
    title = "ECML PKDD 2016 Journal Track Special Issue",
    year = "2016",
    journal = "Data Mining and Knowledge Discovery",
    volume = "30",
    number = "5",
    month = "September",
    publisher = "Springer",
    url = "http://link.springer.com/journal/10618/30/5/page/1",
}
Special Issue of the ECMLPKDD 2016 Journal Track Thomas Gaertner, Mirco Nanni, Andrea Passerini, and Celine Robardet. In Machine Learning Journal 104(2-3).
@article {eclmpkdd_mlj16,
    author = { Gaertner, Thomas and Nanni, Mirco and Passerini, Andrea and Robardet, Celine },
    title = "Special Issue of the ECMLPKDD 2016 Journal Track",
    year = "2016",
    journal = "Machine Learning Journal",
    volume = "104",
    number = "2-3",
    month = "September",
    publisher = "Springer",
    url = "http://link.springer.com/journal/10994/104/2/page/1",
}
Learning Modulo Theories Andrea Passerini. In Unknown venue (type=incollection).
@incollection {lmt16,
    author = { Passerini, Andrea },
    title = "Learning Modulo Theories",
    booktitle = "Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach",
    year = "2016",
    publisher = "Springer International Publishing",
    pages = "113--146",
    url = "papers/lmt16.pdf",
}
Interpretability in Linear Brain Decoding Seyed Mostafa Kia and Andrea Passerini. In ICML Workshop on Human Interpretability in Machine Learning (WHI 2016).
@inproceedings {whi2016,
    author = { Kia, Seyed Mostafa and Passerini, Andrea },
    title = "Interpretability in Linear Brain Decoding",
    booktitle = "ICML Workshop on Human Interpretability in Machine Learning (WHI 2016)",
    url = "https://arxiv.org/pdf/1606.05672.pdf",
    year = "2016",
}
Hashing-Based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report Vaishak Belle, Guy {Van den Broeck}, and Andrea Passerini. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), Sister Conference Best Paper Track.
@inproceedings {BelleIJCAI16,
    author = { Belle, Vaishak and {Van den Broeck}, Guy and Passerini, Andrea },
    title = "Hashing-Based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report",
    booktitle = "Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), Sister Conference Best Paper Track",
    url = "papers/BelleIJCAI16.pdf",
    year = "2016",
    keywords = "conference,selected",
}
Constructive Preference Elicitation by Setwise Max-Margin Learning Stefano Teso, Andrea Passerini, and Paolo Viappiani. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, {IJCAI} 2016, New York, NY, USA, 9-15 July 2016.
@inproceedings {ijcai2016,
    author = { Teso, Stefano and Passerini, Andrea and Viappiani, Paolo },
    title = "Constructive Preference Elicitation by Setwise Max-Margin Learning",
    booktitle = "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, {IJCAI} 2016, New York, NY, USA, 9-15 July 2016",
    pages = "2067--2073",
    year = "2016",
    url = "papers/ijcai16.pdf",
}
Classtering: Joint Classification and Clustering with Mixture of Factor Analysers E. Sansone, A. Passerini, and F. De Natale. In Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI).
@inproceedings {ecai16,
    author = { Sansone, E. and Passerini, A. and Natale, F. De },
    title = "Classtering: Joint Classification and Clustering with Mixture of Factor Analysers",
    url = "papers/ecai16.pdf",
    booktitle = "Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI)",
    year = "2016",
}
Structured Feedback for Preference Elicitation in Complex Domains Stefano Teso, Paolo Dragone, and Andrea Passerini. In BeyondLabeler Workshop at IJCAI 2016.
@inproceedings {beyond16,
    author = { Teso, Stefano and Dragone, Paolo and Passerini, Andrea },
    title = "Structured Feedback for Preference Elicitation in Complex Domains",
    booktitle = "BeyondLabeler Workshop at IJCAI 2016",
    year = "2016",
    url = "papers/beyond16.pdf",
}
Component Caching in Hybrid Domains with Piecewise Polynomial Densities Vaishak Belle, Guy Broeck, and Andrea Passerini. In Proceedings of the 30th Conference on Artificial Intelligence (AAAI).
@inproceedings {BelleAAAI16,
    author = { Belle, Vaishak and Van den Broeck, Guy and Passerini, Andrea },
    title = "Component Caching in Hybrid Domains with Piecewise Polynomial Densities",
    booktitle = "Proceedings of the 30th Conference on Artificial Intelligence (AAAI)",
    year = "2016",
    url = "papers/aaai16.pdf",
    keywords = "conference,strong,selected",
}
RNAcommender: genome-wide recommendation of RNA-protein interactions G. Corrado, T. Tebaldi, F. Costa, P. Frasconi, and A. Passerini. In Bioinformatics.
@article {bioinfo16,
    author = { Corrado, G. and Tebaldi, T. and Costa, F. and Frasconi, P. and Passerini, A. },
    title = "RNAcommender: genome-wide recommendation of RNA-protein interactions",
    journal = "Bioinformatics",
    url = "papers/bioinfo16.pdf",
    year = "2016",
}
Constructive Layout Synthesis via Coactive Learning P. Dragone, L. Erculiani, M.T. Chietera, S. Teso, and A. Passerini. In NIPS Workshop on Constructive Machine Learning.
@inproceedings {cml2016,
    author = { Dragone, P. and Erculiani, L. and Chietera, M.T. and Teso, S. and Passerini, A. },
    title = "Constructive Layout Synthesis via Coactive Learning",
    booktitle = "NIPS Workshop on Constructive Machine Learning",
    url = "papers/cml16.pdf",
    year = "2016",
}
2015

Inducing Sparse Programs for Learning Modulo Theories S. Teso and A. Passerini. In ICML Workshop on Constructive Machine Learning.
@inproceedings {cmlcl2015,
    author = { Teso, S. and Passerini, A. },
    title = "Inducing Sparse Programs for Learning Modulo Theories",
    booktitle = "ICML Workshop on Constructive Machine Learning",
    url = "papers/cml2015cl.pdf",
    year = "2015",
}
Constructive Learning Modulo Theories S. Teso, R. Sebastiani, and A. Passerini. In ICML Workshop on Constructive Machine Learning.
@inproceedings {cmllmt2015,
    author = { Teso, S. and Sebastiani, R. and Passerini, A. },
    title = "Constructive Learning Modulo Theories",
    booktitle = "ICML Workshop on Constructive Machine Learning",
    url = "papers/cml2015lmt.pdf",
    year = "2015",
}
Bootstrapping Domain Ontologies from Wikipedia: A Uniform Approach Daniil Mirylenka, Andrea Passerini, and Luciano Serafini. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, {IJCAI} 2015, Buenos Aires, Argentina, July 25-31, 2015.
@inproceedings {ijcai_myr2015,
    author = { Mirylenka, Daniil and Passerini, Andrea and Serafini, Luciano },
    title = "Bootstrapping Domain Ontologies from Wikipedia: {A} Uniform Approach",
    booktitle = "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, {IJCAI} 2015, Buenos Aires, Argentina, July 25-31, 2015",
    pages = "1464--1470",
    year = "2015",
    url = "papers/ijcai2015wiki.pdf",
    timestamp = "Mon, 20 Jul 2015 19:12:40 +0200",
    biburl = "http://dblp.uni-trier.de/rec/bib/conf/ijcai/MirylenkaPS15",
    bibsource = "dblp computer science bibliography, http://dblp.org",
}
Probabilistic Inference in Hybrid Domains by Weighted Model Integration Vaishak Belle, Andrea Passerini, and Guy {Van den Broeck}. In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI).
@inproceedings {ijcai_bel2015,
    author = { Belle, Vaishak and Passerini, Andrea and {Van den Broeck}, Guy },
    title = "Probabilistic Inference in Hybrid Domains by Weighted Model Integration",
    booktitle = "Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI)",
    year = "2015",
    url = "papers/ijcai2015wmi.pdf",
    keywords = "conference,strong,selected",
}
Three distinct ribosome assemblies modulated by translation are the building blocks of polysomes Gabriella Viero, Lorenzo Lunelli, Andrea Passerini, Paolo Bianchini, Robert J. Gilbert, Paola Bernabo', Toma Tebaldi, Alberto Diaspro, Cecilia Pederzolli, and Alessandro Quattrone. In The Journal of Cell Biology 208(5).
@article {jcb2015,
    author = { Viero, Gabriella and Lunelli, Lorenzo and Passerini, Andrea and Bianchini, Paolo and Gilbert, Robert J. and Bernabo', Paola and Tebaldi, Toma and Diaspro, Alberto and Pederzolli, Cecilia and Quattrone, Alessandro },
    title = "Three distinct ribosome assemblies modulated by translation are the building blocks of polysomes",
    volume = "208",
    number = "5",
    pages = "581-596",
    year = "2015",
    doi = "10.1083/jcb.201406040",
    eprint = "http://jcb.rupress.org/content/208/5/581.full.pdf+html",
    url = "papers/jcb2015.pdf",
    journal = "The Journal of Cell Biology",
}
Hashing-Based Approximate Probabilistic Inference in Hybrid Domains Vaishak Belle, Guy {Van den Broeck}, and Andrea Passerini. In Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI).
@inproceedings {uai2015,
    author = { Belle, Vaishak and {Van den Broeck}, Guy and Passerini, Andrea },
    title = "Hashing-Based Approximate Probabilistic Inference in Hybrid Domains",
    booktitle = "Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI)",
    url = "papers/uai2015.pdf",
    year = "2015",
    annotation = "(UAI best paper award)",
    keywords = "conference,strong,selected",
}
2014

Predicting virus mutations through statistical relational learning. Elisa Cilia, Stefano Teso, Sergio Ammendola, Tom Lenaerts, and Andrea Passerini. In BMC Bioinformatics 15(1).
@article {Cilia2014,
    author = { Cilia, Elisa and Teso, Stefano and Ammendola, Sergio and Lenaerts, Tom and Passerini, Andrea },
    title = "Predicting virus mutations through statistical relational learning.",
    journal = "BMC Bioinformatics",
    year = "2014",
    volume = "15",
    pages = "309",
    number = "1",
    url = "papers/bmcbioinfo14_frankie.pdf",
    doi = "10.1186/1471-2105-15-309",
    keywords = "25238967",
    owner = "andrea",
    pii = "1471-2105-15-309",
    pmid = "25238967",
    timestamp = "2014.10.22",
}
Joint Probabilistic-Logical Refinement of Multiple Protein Feature Predictors S. Teso and A. Passerini. In BMC-Bioinformatics.
@article {bmcbioinfo14_mln,
    author = { Teso, S. and Passerini, A. },
    title = "Joint Probabilistic-Logical Refinement of Multiple Protein Feature Predictors",
    journal = "BMC-Bioinformatics",
    year = "2014",
    url = "papers/bmcbioinfo14_mln.pdf",
    volume = "15:16",
}
Improved multi-level protein-protein interaction prediction with semantic-based regularization. Claudio Sacca', Stefano Teso, Michelangelo Diligenti, and Andrea Passerini. In BMC Bioinformatics.
@article {bmcbioinformatics14_sbr,
    author = { Sacca', Claudio and Teso, Stefano and Diligenti, Michelangelo and Passerini, Andrea },
    url = "papers/bmcbioinformatics14_sbr.pdf",
    title = "Improved multi-level protein-protein interaction prediction with semantic-based regularization.",
    journal = "BMC Bioinformatics",
    year = "2014",
    volume = "15",
    pages = "103",
    doi = "10.1186/1471-2105-15-103",
    keywords = "Artificial Intelligence, Models, Molecular, Protein Binding, Protein Interaction Domains and Motifs, Proteins, Semantics, Software, 20817744",
    owner = "andrea",
    pii = "1471-2105-15-103",
    pmid = "20817744",
    timestamp = "2014.07.11",
}
PTRcombiner: mining combinatorial regulation of gene expression from post-transcriptional interaction maps. Gianluca Corrado, Toma Tebaldi, Giulio Bertamini, Fabrizio Costa, Alessandro Quattrone, Gabriella Viero, and Andrea Passerini. In BMC Genomics.
@article {bmcgenetics14,
    author = { Corrado, Gianluca and Tebaldi, Toma and Bertamini, Giulio and Costa, Fabrizio and Quattrone, Alessandro and Viero, Gabriella and Passerini, Andrea },
    title = "P{TR}combiner: mining combinatorial regulation of gene expression from post-transcriptional interaction maps.",
    journal = "BMC Genomics",
    year = "2014",
    volume = "15",
    pages = "304",
    url = "papers/bmcgenomics14.pdf",
    doi = "10.1186/1471-2164-15-304",
    keywords = "24758252",
    owner = "andrea",
    pii = "1471-2164-15-304",
    pmid = "24758252",
    timestamp = "2014.07.11",
}
Improving Activity Recognition by Segmental Pattern Mining U. Avci and A. Passerini. In IEEE Transactions on Knowledge and Data Engineering 26(4).
@article {tkde2014,
    author = { Avci, U. and Passerini, A. },
    title = "Improving Activity Recognition by Segmental Pattern Mining",
    journal = "IEEE Transactions on Knowledge and Data Engineering",
    volume = "26",
    number = "4",
    pages = "889--902",
    url = "papers/tkde2014.pdf",
    year = "2014",
}
2013

Type Extension Trees for Feature Construction and Learning in Relational Domains M. Jaeger, M. Lippi, A. Passerini, and P. Frasconi. In Artificial Intelligence Journal 204(30--55).
@article {aij13,
    author = { Jaeger, M. and Lippi, M. and Passerini, A. and Frasconi, P. },
    title = "Type Extension Trees for Feature Construction and Learning in Relational Domains",
    journal = "Artificial Intelligence Journal",
    year = "2013",
    volume = "204",
    url = "papers/aij13.pdf",
    number = "30--55",
}
Navigating the topical structure of academic search results via Wikipedia category network D. Mirylenka and A. Passerini. In ACM International Conference on Information and Knowledge Management (CIKM 2013).
@inproceedings {cikm2013,
    author = { Mirylenka, D. and Passerini, A. },
    title = "Navigating the topical structure of academic search results via Wikipedia category network",
    booktitle = "ACM International Conference on Information and Knowledge Management (CIKM 2013)",
    year = "2013",
    url = "papers/cikm2013.pdf",
    address = "San Francisco, CA, USA",
}
Supervised graph summarization for structuring academic search results D. Mirylenka and A. Passerini. In NIPS Workshop on Constructive Machine Learning.
@inproceedings {nips2013myr,
    author = { Mirylenka, D. and Passerini, A. },
    title = "Supervised graph summarization for structuring academic search results",
    booktitle = "NIPS Workshop on Constructive Machine Learning",
    url = "papers/cml2013myr.pdf",
    year = "2013",
}
Hybrid SRL with Optimization Modulo Theories S. Teso, R. Sebastiani, and A. Passerini. In NIPS Workshop on Constructive Machine Learning.
@inproceedings {nips2013teso,
    author = { Teso, S. and Sebastiani, R. and Passerini, A. },
    title = "Hybrid SRL with Optimization Modulo Theories",
    booktitle = "NIPS Workshop on Constructive Machine Learning",
    url = "papers/cml2013teso.pdf",
    year = "2013",
}
ScienScan -- an efficient visualization and browsing tool for academic search D. Mirylenka and A. Passerini. In Machine Learning and Knowledge Discovery in Databases (ECML/PKDD'13, Demo Track).
@inproceedings {ecml2013,
    author = { Mirylenka, D. and Passerini, A. },
    title = "ScienScan -- an efficient visualization and browsing tool for academic search",
    booktitle = "Machine Learning and Knowledge Discovery in Databases (ECML/PKDD'13, Demo Track)",
    year = "2013",
    url = "papers/ecml2013.pdf",
    address = "Prague, Czech Republic",
}
A Fully Unsupervised Approach to Activity Discovery U. Avci and A. Passerini. In ACM Multimedia workshop on Human Behavior Understanding (HBU 2013).
@inproceedings {hbu2013,
    author = { Avci, U. and Passerini, A. },
    title = "A Fully Unsupervised Approach to Activity Discovery",
    booktitle = "ACM Multimedia workshop on Human Behavior Understanding (HBU 2013)",
    year = "2013",
    url = "papers/hbu2013.pdf",
    address = "Barcelona, Spain",
}
Active Learning of Pareto Fronts with Disconnected Feasible Decision and Objective Spaces P. Campigotto, A. Passerini, and R. Battiti. In Metaheuristics International Conference (MIC 2013).
@inproceedings {mic2013alp,
    author = { Campigotto, P. and Passerini, A. and Battiti, R. },
    title = "Active Learning of Pareto Fronts with Disconnected Feasible Decision and Objective Spaces",
    booktitle = "Metaheuristics International Conference (MIC 2013)",
    year = "2013",
    note = "(extended abstract)",
    url = "papers/mic2013alp.pdf",
    address = "Singapore",
}
Learning to Diversify in Complex Interactive Multiobjective Optimization D. Mukhlisullina, A. Passerini, and R. Battiti. In Metaheuristics International Conference (MIC 2013).
@inproceedings {mic2013bcmoead,
    author = { Mukhlisullina, D. and Passerini, A. and Battiti, R. },
    title = "Learning to Diversify in Complex Interactive Multiobjective Optimization",
    booktitle = "Metaheuristics International Conference (MIC 2013)",
    year = "2013",
    note = "(best paper award)",
    url = "papers/mic2013bcmoead.pdf",
    address = "Singapore",
}
Kernel Methods for Structured Data Andrea Passerini. In Handbook on Neural Information Processing (Intelligent Systems Reference Library).
@inproceedings {PassHandNIP13,
    author = { Passerini, Andrea },
    year = "2013",
    isbn = "978-3-642-36656-7",
    booktitle = "Handbook on Neural Information Processing",
    volume = "49",
    series = "Intelligent Systems Reference Library",
    doi = "10.1007/978-3-642-36657-4_9",
    title = "Kernel Methods for Structured Data",
    url = "papers/nipchap.pdf",
    publisher = "Springer Berlin Heidelberg",
    pages = "283-333",
    language = "English",
}
Learning to Grow Structured Visual Summaries for Document Collections D. Mirylenka and A. Passerini. In ICML Workshop on Structured Learning: Inferring Graphs from Structured and Unstructured Inputs.
@inproceedings {slg2013,
    author = { Mirylenka, D. and Passerini, A. },
    title = "Learning to Grow Structured Visual Summaries for Document Collections",
    booktitle = "ICML Workshop on Structured Learning: Inferring Graphs from Structured and Unstructured Inputs",
    year = "2013",
    url = "papers/slg2013.pdf",
    address = "Atlanta, GA, USA",
}
Ego-Centric Graphlets for Personality and Affective States Recognition S. Teso, J. Staiano, B. Lepri, A. Passerini, and F. Pianesi. In ASE/IEEE International Conference on Social Computing.
@inproceedings {soccom2013,
    author = { Teso, S. and Staiano, J. and Lepri, B. and Passerini, A. and Pianesi, F. },
    title = "Ego-Centric Graphlets for Personality and Affective States Recognition",
    booktitle = "ASE/IEEE International Conference on Social Computing",
    year = "2013",
    url = "papers/soccom2013.pdf",
    address = "Washington D.C., USA",
}
Active learning of Pareto fronts P. Campigotto, A. Passerini, and R. Battiti. In IEEE Transactions on Neural Networks and Learning Systems 25(3).
@article {tnn2013,
    author = { Campigotto, P. and Passerini, A. and Battiti, R. },
    title = "Active learning of Pareto fronts",
    journal = "IEEE Transactions on Neural Networks and Learning Systems",
    year = "2013",
    volume = "25",
    number = "3",
    url = "papers/tnn2013.pdf",
    pages = "506--519",
}
Ego-Centric Graphlets for Personality and Affective States Recognition S. Teso, J. Staiano, B. Lepri, A. Passerini, and F. Pianesi. In Workshop on Information in Networks (WIN 2013).
@inproceedings {win2013,
    author = { Teso, S. and Staiano, J. and Lepri, B. and Passerini, A. and Pianesi, F. },
    title = "Ego-Centric Graphlets for Personality and Affective States Recognition",
    booktitle = "Workshop on Information in Networks (WIN 2013)",
    year = "2013",
    url = "papers/win2013.pdf",
    note = "(abstract)",
}
2012

Predicting Metal-Binding Sites from Protein Sequence Andrea Passerini, Marco Lippi, and Paolo Frasconi. In IEEE/ACM Trans. Comput. Biol. Bioinformatics.
@article {ieeetccb11,
    author = { Passerini, Andrea and Lippi, Marco and Frasconi, Paolo },
    title = "Predicting Metal-Binding Sites from Protein Sequence",
    journal = "IEEE/ACM Trans. Comput. Biol. Bioinformatics",
    issue_date = "January 2012",
    volume = "9",
    issue = "1",
    month = "January",
    year = "2012",
    issn = "1545-5963",
    pages = "203--213",
    numpages = "11",
    doi = "http://dx.doi.org/10.1109/TCBB.2011.94",
    acmid = "2077958",
    publisher = "IEEE Computer Society Press",
    address = "Los Alamitos, CA, USA",
    url = "papers/ieeetccb11.pdf",
    keywords = "Metal-binding prediction, machine learning, structured-output learning, greedy algorithms.",
}
Predicting virus mutations through relational learning Elisa Cilia, Stefano Teso, Sergio Ammendola, Tom Lenaerts, and Andrea Passerini. In ECCB Workshop on Annotation, Interpretation and Management of Mutations (AIMM-2012).
@inproceedings {aimm12,
    author = { Cilia, Elisa and Teso, Stefano and Ammendola, Sergio and Lenaerts, Tom and Passerini, Andrea },
    title = "Predicting virus mutations through relational learning",
    booktitle = "ECCB Workshop on Annotation, Interpretation and Management of Mutations (AIMM-2012)",
    year = "2012",
    url = "papers/aimm12.pdf",
    bibsource = "DBLP, http://dblp.uni-trier.de",
}
Widespread translational control uncouples transcriptome and translatome profiles in mammalian cells T. Tebaldi, A. Re, G. Viero, I. Pegoretti, A. Passerini, E. Blanzieri, and A. Quattrone. In BMC Genomics.
@article {bmcgenomics12,
    author = { Tebaldi, T. and Re, A. and Viero, G. and Pegoretti, I. and Passerini, A. and Blanzieri, E. and Quattrone, A. },
    title = "Widespread translational control uncouples transcriptome and translatome profiles in mammalian cells",
    journal = "BMC Genomics",
    year = "2012",
    volume = "13:220",
    url = "papers/bmcgenomics12.pdf",
    optnumber = "",
    optpages = "",
    optmonth = "",
    optnote = "",
    optannote = "",
}
Metal binding in proteins: machine learning complements X-ray absorption spectroscopy M. Lippi, A. Passerini, M. Punta, and P. Frasconi. In Machine Learning and Knowledge Discovery in Databases (ECML/PKDD'12, Nectar Track) (Lecture Notes in Computer Science).
@inproceedings {nectar12,
    author = { Lippi, M. and Passerini, A. and Punta, M. and Frasconi, P. },
    title = "Metal binding in proteins: machine learning complements X-ray absorption spectroscopy",
    doi = "10.1007/978-3-642-33486-3_63",
    publisher = "Springer Berlin Heidelberg",
    isbn = "978-3-642-33485-6",
    booktitle = "Machine Learning and Knowledge Discovery in Databases (ECML/PKDD'12, Nectar Track)",
    volume = "7524",
    series = "Lecture Notes in Computer Science",
    url = "papers/nectar12.pdf",
    year = "2012",
}
Improving Activity Recognition by Segmental Pattern Mining U. Avci and A. Passerini. In PerCOM'2012 Workshop on PervasivE Learning, Life, and Leisure.
@inproceedings {perel012,
    author = { Avci, U. and Passerini, A. },
    title = "Improving Activity Recognition by Segmental Pattern Mining",
    booktitle = "PerCOM'2012 Workshop on PervasivE Learning, Life, and Leisure",
    url = "papers/perel012.pdf",
    year = "2012",
}
2011

Relational Feature Mining with Hierarchical Multitask kFOIL Elisa Cilia, Neils Landwehr, and Andrea Passerini. In Fundamenta Informaticae 113(2).
@article {fundinf11,
    author = { Cilia, Elisa and Landwehr, Neils and Passerini, Andrea },
    title = "Relational Feature Mining with Hierarchical Multitask kFOIL",
    journal = "Fundamenta Informaticae",
    month = "December",
    year = "2011",
    volume = "113",
    number = "2",
    url = "papers/fundinf11.pdf",
    pages = "151--177",
}
Preference elicitation for interactive learning of Optimization Modulo Theory problems P. Campigotto, A. Passerini, and R. Battiti. In NIPS'11 workshop on Choice Models and Preference Learning.
@inproceedings {cmpl11,
    author = { Campigotto, P. and Passerini, A. and Battiti, R. },
    title = "Preference elicitation for interactive learning of Optimization Modulo Theory problems",
    booktitle = "NIPS'11 workshop on Choice Models and Preference Learning",
    url = "papers/cmpl11.pdf",
    year = "2011",
}
Characterization of metalloproteins by high-throughput X-ray absorption spectroscopy. W. Shi, M. Punta, J. Bohon, J.M. Sauder, R. D'Mello, M. Sullivan, J. Toomey, D. Abel, M. Lippi, A. Passerini, P. Frasconi, S.K. Burley, B. Rost, and M.R. Chance. In Genome Res 21(6).
@article {genomeres11,
    author = { Shi, W. and Punta, M. and Bohon, J. and Sauder, J.M. and D'Mello, R. and Sullivan, M. and Toomey, J. and Abel, D. and Lippi, M. and Passerini, A. and Frasconi, P. and Burley, S.K. and Rost, B. and Chance, M.R. },
    title = "Characterization of metalloproteins by high-throughput X-ray absorption spectroscopy.",
    journal = "Genome Res",
    volume = "21",
    number = "6",
    pages = "898-907",
    year = "2011",
    url = "papers/genomeres11.pdf",
}
Active Learning of Combinatorial Features for Interactive Optimization Paolo Campigotto, Andrea Passerini, and Roberto Battiti. In Proceedings of the 5th international conference on Learning and Intelligent Optimization.
@inproceedings {lion11,
    author = { Campigotto, Paolo and Passerini, Andrea and Battiti, Roberto },
    title = "Active Learning of Combinatorial Features for Interactive Optimization",
    booktitle = "Proceedings of the 5th international conference on Learning and Intelligent Optimization",
    year = "2011",
    url = "papers/lion11.pdf",
    pages = "336-350",
}
Relational information gain M. Lippi, M. Jaeger, P. Frasconi, and A. Passerini. In Machine Learning.
@article {mlj11,
    author = { Lippi, M. and Jaeger, M. and Frasconi, P. and Passerini, A. },
    title = "Relational information gain",
    journal = "Machine Learning",
    volume = "83",
    url = "papers/mlj11.pdf",
    pages = "219--239",
    year = "2011",
}
MetalDetector v2.0: predicting the geometry of metal binding sites from protein sequence. A. Passerini, M. Lippi, and P. Frasconi. In Nucleic Acids Res 39(Web Server issue).
@article {nar11,
    author = { Passerini, A. and Lippi, M. and Frasconi, P. },
    title = "MetalDetector v2.0: predicting the geometry of metal binding sites from protein sequence.",
    journal = "Nucleic Acids Res",
    volume = "39",
    url = "papers/nar11.pdf",
    number = "Web Server issue",
    pages = "W288-92",
    year = "2011",
}
2010

Predicting structural and functional sites in proteins by searching for maximum-weight cliques F. Mascia, E. Cilia, M. Brunato, and A. Passerini. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10).
@inproceedings {aaai10,
    author = { Mascia, F. and Cilia, E. and Brunato, M. and Passerini, A. },
    title = "Predicting structural and functional sites in proteins by searching for maximum-weight cliques",
    booktitle = "Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10)",
    url = "papers/aaai10.pdf",
    year = "2010",
}
Frankenstein Junior: a relational learning approach toward protein engineering E. Cilia and A. Passerini. In ECCB 2010 Workshop on Annotation, Interpretation, and Management of Mutations (AIMM 2010).
@inproceedings {aimm10,
    author = { Cilia, E. and Passerini, A. },
    title = "Frankenstein Junior: a relational learning approach toward protein engineering",
    booktitle = "ECCB 2010 Workshop on Annotation, Interpretation, and Management of Mutations (AIMM 2010)",
    year = "2010",
    url = "papers/aimm10.pdf",
    address = "Ghent (Belgium)",
}
Automatic prediction of catalytic residues by modeling residue structural neighborhood Elisa Cilia and Andrea Passerini. In BMC Bioinformatics 11(1).
@article {bmc10,
    author = { Cilia, Elisa and Passerini, Andrea },
    title = "Automatic prediction of catalytic residues by modeling residue structural neighborhood",
    journal = "BMC Bioinformatics",
    volume = "11",
    year = "2010",
    number = "1",
    pages = "115",
    url = "papers/bmc10.pdf",
    doi = "10.1186/1471-2105-11-115",
}
Handling concept drift in preference learning for interactive decision making P. Campigotto, A. Passerini, and R. Battiti. In ECML/PKDD 2010 Workshop on Handling Concept Drift in Adaptive Information Systems (HaCDAIS 2010).
@inproceedings {hacdais10,
    author = { Campigotto, P. and Passerini, A. and Battiti, R. },
    title = "Handling concept drift in preference learning for interactive decision making",
    booktitle = "ECML/PKDD 2010 Workshop on Handling Concept Drift in Adaptive Information Systems (HaCDAIS 2010)",
    year = "2010",
    url = "papers/hacdais10.pdf",
    address = "Barcelona (Spain)",
}
From on-going to complete activity recognition exploiting related activities C. Nicolini, B. Lepri, S. Teso, and A. Passerini. In International Workshop on Human Behavour Understanding (HBU'10).
@inproceedings {hbu10,
    author = { Nicolini, C. and Lepri, B. and Teso, S. and Passerini, A. },
    title = "From on-going to complete activity recognition exploiting related activities",
    booktitle = "International Workshop on Human Behavour Understanding (HBU'10)",
    url = "papers/hbu10.pdf",
    year = "2010",
}
Adapting to a realistic decision maker: experiments towards a reactive multi-objective optimizer P. Campigotto and A. Passerini. In LION workshop on Multiobjective Metaheuristics (LION-MOME).
@inproceedings {lion-mome10,
    author = { Campigotto, P. and Passerini, A. },
    title = "Adapting to a realistic decision maker: experiments towards a reactive multi-objective optimizer",
    booktitle = "LION workshop on Multiobjective Metaheuristics (LION-MOME)",
    url = "papers/lion-mome10.pdf",
    year = "2010",
}
Fast learning of relational kernels N. Landwehr, A. Passerini, L. {De Raedt}, and P. Frasconi. In Machine Learning 79(3).
@article {mlj10,
    author = { Landwehr, N. and Passerini, A. and {De Raedt}, L. and Frasconi, P. },
    title = "Fast learning of relational kernels",
    journal = "Machine Learning",
    pages = "305--342",
    url = "papers/mlj10.pdf",
    publisher = "Springer",
    volume = "79",
    number = "3",
    year = "2010",
    doi = "10.1007/s10994-009-5163-1",
}
An On/Off Lattice Approach to Protein Structure Prediction from Contact Maps S. Teso, C. Di Risio, A. Passerini, and R. Battiti. In Proceedings of Pattern Recognition in Bioinformatics (PRIB2010) (Lecture Notes in Bioinformatics (LNBI)).
@inproceedings {prib10,
    author = { Teso, S. and Risio, C. Di and Passerini, A. and Battiti, R. },
    title = "An On/Off Lattice Approach to Protein Structure Prediction from Contact Maps",
    booktitle = "Proceedings of Pattern Recognition in Bioinformatics (PRIB2010)",
    year = "2010",
    series = "Lecture Notes in Bioinformatics (LNBI)",
    url = "papers/prib10.pdf",
    publisher = "Springer",
}
Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker R. Battiti and A. Passerini. In IEEE Transactions on Evolutionary Computation.
@article {tevo10,
    author = { Battiti, R. and Passerini, A. },
    title = "Brain-Computer Evolutionary Multi-Objective Optimization (BC-EMO): a genetic algorithm adapting to the decision maker",
    journal = "IEEE Transactions on Evolutionary Computation",
    url = "papers/tevo10.pdf",
    year = "2010",
}
2009

Mining Drug Resistance Relational Features with Hierarchical Multitask kFOIL Elisa Cilia, Niels Landwehr, and Andrea Passerini. In Proceedings of BioLogical@AI*IA2009.
@inproceedings {biological09,
    author = { Cilia, Elisa and Landwehr, Niels and Passerini, Andrea },
    title = "Mining Drug Resistance Relational Features with Hierarchical Multitask kFOIL",
    booktitle = "Proceedings of BioLogical@AI*IA2009",
    month = "December",
    year = "2009",
}
Relational Information Gain M. Lippi, M. Jaeger, P. Frasconi, and A. Passerini. In 19th International Conference on Inductive Logic Programming (ILP'09).
@inproceedings {ilp09,
    author = { Lippi, M. and Jaeger, M. and Frasconi, P. and Passerini, A. },
    title = "Relational Information Gain",
    booktitle = "19th International Conference on Inductive Logic Programming (ILP'09)",
    year = "2009",
}
Predicting the Geometry of Metal Binding Sites from Protein Sequence P. Frasconi and A. Passerini. In Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS'08).
@inproceedings {nips08,
    author = { Frasconi, P. and Passerini, A. },
    title = "Predicting the Geometry of Metal Binding Sites from Protein Sequence",
    booktitle = "Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS'08)",
    pages = "465--472",
    year = "2009",
}
2008

A semiparametric generative model for efficient structured-output supervised learning F. Costa, A. Passerini, M. Lippi, and P. Frasconi. In Annals of Mathematics and Artificial Intelligence 54(1-3).
@article {amai08,
    author = { Costa, F. and Passerini, A. and Lippi, M. and Frasconi, P. },
    title = "A semiparametric generative model for efficient structured-output supervised learning",
    journal = "Annals of Mathematics and Artificial Intelligence",
    volume = "54",
    number = "1-3",
    year = "2008",
    issn = "1012-2443",
    pages = "207--222",
    doi = "http://dx.doi.org/10.1007/s10472-009-9137-6",
    publisher = "Kluwer Academic Publishers",
    address = "Hingham, MA, USA",
}
Learning with Kernels and Logical Representations P. Frasconi and A. Passerini. In Unknown venue (type=incollection).
@incollection {aprilchap08,
    author = { Frasconi, P. and Passerini, A. },
    title = "Learning with Kernels and Logical Representations",
    booktitle = "Probabilistic Inductive Logic Programming: Theory and Application",
    publisher = "Springer",
    year = "2008",
    pages = "56--91",
    volume = "LNAI 4911",
}
On the Convergence of Protein Structure and Dynamics. Statistical Learning Studies of Pseudo Folding Pathways A. Vullo, A. Passerini, P. Frasconi, F. Costa, and G. Pollastri. In 6th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EVOBIO'08).
@inproceedings {evobio08,
    author = { Vullo, A. and Passerini, A. and Frasconi, P. and Costa, F. and Pollastri, G. },
    title = "On the Convergence of Protein Structure and Dynamics. Statistical Learning Studies of Pseudo Folding Pathways",
    booktitle = "6th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EVOBIO'08)",
    year = "2008",
}
A simplified approach to disulfide connectivity prediction from protein sequences M. Vincent, A. Passerini, M. Labb\`e, and P. Frasconi. In BMC Bioinformatics 9(20).
@article {bmc08,
    author = { Vincent, M. and Passerini, A. and Labb\`e, M. and Frasconi, P. },
    title = "A simplified approach to disulfide connectivity prediction from protein sequences",
    journal = "BMC Bioinformatics",
    year = "2008",
    volume = "9",
    number = "20",
}
MetalDetector: a web server for predicting metal binding sites and disulfide bridges in proteins from sequence M. Lippi, A. Passerini, M. Punta, B. Rost, and P. Frasconi. In Bioinformatics 24(18).
@article {bioinfo08,
    author = { Lippi, M. and Passerini, A. and Punta, M. and Rost, B. and Frasconi, P. },
    title = "MetalDetector: a web server for predicting metal binding sites and disulfide bridges in proteins from sequence",
    journal = "Bioinformatics",
    year = "2008",
    volume = "24",
    number = "18",
    pages = "2094--2095",
}
Feature Discovery with Type Extension Trees P. Frasconi, M. Jaeger, and A. Passerini. In 18th International Conference on Inductive Logic Programming (ILP'08).
@inproceedings {ilp08,
    author = { Frasconi, P. and Jaeger, M. and Passerini, A. },
    title = "Feature Discovery with Type Extension Trees",
    booktitle = "18th International Conference on Inductive Logic Programming (ILP'08)",
    year = "2008",
}
Learning Type Extension Trees for Metal Bonding State Prediction P. Frasconi, M. Jaeger, and A. Passerini. In ECML'08 Workshop on Statistical and Relational Learning in Bioinformatics.
@inproceedings {ecml08,
    author = { Frasconi, P. and Jaeger, M. and Passerini, A. },
    title = "Learning Type Extension Trees for Metal Bonding State Prediction",
    booktitle = "ECML'08 Workshop on Statistical and Relational Learning in Bioinformatics",
    year = "2008",
}
2007

Predicting zinc binding at the proteome level A. Passerini, C. Andreini, S. Menchetti, A. Rosato, and P. Frasconi. In BMC Bioinformatics 8(39).
@article {bmc07,
    author = { Passerini, A. and Andreini, C. and Menchetti, S. and Rosato, A. and Frasconi, P. },
    title = "Predicting zinc binding at the proteome level",
    journal = "BMC Bioinformatics",
    year = "2007",
    volume = "8",
    number = "39",
}
Automatic Classification of Provisions in Legislative Texts E. Francesconi and A. Passerini. In Artificial Intelligence and Law 15(1).
@article {ailaw07,
    author = { Francesconi, E. and Passerini, A. },
    title = "Automatic Classification of Provisions in Legislative Texts",
    journal = "Artificial Intelligence and Law",
    year = "2007",
    volume = "15",
    number = "1",
    pages = "1--17",
}
Machine Learning in Structural Genomics A. Passerini and A. Vullo. In Unknown venue (type=incollection).
@incollection {angeli07,
    author = { Passerini, A. and Vullo, A. },
    title = "Machine Learning in Structural Genomics",
    booktitle = "Bioinformatica: sfide e prospettive",
    publisher = "Franco Angeli Press",
    year = "2007",
}
Proof Tree Kernels: a Candidate Ingredient for Intelligent Optimization A. Passerini and P. Frasconi. In Learning and Intelligent OptimizatioN - LION 2007 II.
@inproceedings {lion07,
    author = { Passerini, A. and Frasconi, P. },
    title = "Proof Tree Kernels: a Candidate Ingredient for Intelligent Optimization",
    booktitle = "Learning and Intelligent OptimizatioN - LION 2007 II",
    year = "2007",
}
2006

Improving Prediction of Zinc Binding Sites by Modeling the Linkage between Residues Close in Sequence S. Menchetti, A. Passerini, P. Frasconi, C. Andreini, and A. Rosato. In Proceedings of RECOMB'06.
@inproceedings {recomb06,
    author = { Menchetti, S. and Passerini, A. and Frasconi, P. and Andreini, C. and Rosato, A. },
    title = "Improving Prediction of Zinc Binding Sites by Modeling the Linkage between Residues Close in Sequence",
    booktitle = "Proceedings of RECOMB'06",
    year = "2006",
    pages = "309--320",
    address = "Venice, Italy, April 2-5",
}
Identifying Cysteines and Histidines in Transition-Metal-Binding Sites Using Support Vector Machines and Neural Networks A. Passerini, M. Punta, A. Ceroni, B. Rost, and P. Frasconi. In PROTEINS: Structure, Functions and Bioinformatics 65(2).
@article {proteins06,
    author = { Passerini, A. and Punta, M. and Ceroni, A. and Rost, B. and Frasconi, P. },
    title = "Identifying Cysteines and Histidines in Transition-Metal-Binding Sites Using Support Vector Machines and Neural Networks",
    journal = "PROTEINS: Structure, Functions and Bioinformatics",
    year = "2006",
    volume = "65",
    number = "2",
    pages = "305--316",
}
DISULFIND: a Disulfide Bonding State and Cysteine Connectivity Prediction Server A. Ceroni, A. Passerini, A. Vullo, and P. Frasconi. In Nucleic Acids Research.
@article {disulfind,
    author = { Ceroni, A. and Passerini, A. and Vullo, A. and Frasconi, P. },
    title = "DISULFIND: a Disulfide Bonding State and Cysteine Connectivity Prediction Server",
    journal = "Nucleic Acids Research",
    year = "2006",
    volume = "34(Web Server Issue)",
    pages = "W177--W181",
}
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting A. Passerini, P. Frasconi, and L. De Raedt. In Journal of Machine Learning Research (Special Topic on Inductive Programming).
@article {jmlr06,
    author = { Passerini, A. and Frasconi, P. and Raedt, L. De },
    title = "Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting",
    journal = "Journal of Machine Learning Research (Special Topic on Inductive Programming)",
    year = "2006",
    volume = "7",
    pages = "307--342",
}
kFOIL: Learning Simple Relational Kernels N. Landwehr, A. Passerini, L. De Raedt, and P. Frasconi. In Proceedings of AAAI'06.
@inproceedings {aaai06,
    author = { Landwehr, N. and Passerini, A. and Raedt, L. De and Frasconi, P. },
    title = "kFOIL: Learning Simple Relational Kernels",
    booktitle = "Proceedings of AAAI'06",
    year = "2006",
    address = "Boston, Massachusetts, USA",
}
Learning Structured Outputs via Kernel Dependency Estimation and Stochastic Grammars F. Costa, A. Passerini, and P. Frasconi. In ECML'06 Workshop on Mining and Learning with Graphs (MLG 2006).
@inproceedings {mlg06,
    author = { Costa, F. and Passerini, A. and Frasconi, P. },
    title = "Learning Structured Outputs via Kernel Dependency Estimation and Stochastic Grammars",
    booktitle = "ECML'06 Workshop on Mining and Learning with Graphs (MLG 2006)",
    year = "2006",
}
Decomposition Kernels for Natural Language Processing F. Costa, S. Menchetti, A. Ceroni, A. Passerini, and P. Frasconi. In EACL'06 Workshop on Learning Structured Information in Natural Language Applications.
@inproceedings {eacl06,
    author = { Costa, F. and Menchetti, S. and Ceroni, A. and Passerini, A. and Frasconi, P. },
    title = "Decomposition Kernels for Natural Language Processing",
    booktitle = "EACL'06 Workshop on Learning Structured Information in Natural Language Applications",
    year = "2006",
}
2005

Kernels on Prolog Ground Terms A. Passerini and P. Frasconi. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence.
@inproceedings {ijcai05,
    author = { Passerini, A. and Frasconi, P. },
    title = "Kernels on Prolog Ground Terms",
    booktitle = "Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence",
    address = "Edinburgh, Scotland, UK",
    year = "2005",
    pages = "1626--1627",
}
Automatic semantics extraction in law documents C. Biagioli, E. Francesconi, A. Passerini, S. Montemagni, and C. Soria. In Proceedings of ICAIL'05.
@inproceedings {icail05,
    author = { Biagioli, C. and Francesconi, E. and Passerini, A. and Montemagni, S. and Soria, C. },
    title = "Automatic semantics extraction in law documents",
    booktitle = "Proceedings of ICAIL'05",
    pages = "133--140",
    year = "2005",
    address = "Bologna, Italy",
}
Declarative Kernels P. Frasconi, A. Passerini, S. Muggleton, and H. Lodhi. In Late-Breaking Papers of the 15th International Conference on inductive Logic Programming (ILP 05).
@inproceedings {ilp05,
    author = { Frasconi, P. and Passerini, A. and Muggleton, S. and Lodhi, H. },
    title = "Declarative Kernels",
    booktitle = "Late-Breaking Papers of the 15th International Conference on inductive Logic Programming (ILP 05)",
    year = "2005",
    address = "Bonn, Germany",
}
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting A. Passerini, P. Frasconi, and L. De Raedt. In ICML '05 Workshop on Approaches and Applications of Inductive Programming.
@inproceedings {aaip05,
    author = { Passerini, A. and Frasconi, P. and Raedt, L. De },
    title = "Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting",
    booktitle = "ICML '05 Workshop on Approaches and Applications of Inductive Programming",
    year = "2005",
}
Kernels for Logic Proof Trees A. Passerini, P. Frasconi, and L. De Raedt. In Dagstuhl Seminar 05051: Probabilistic, Logical and Relational Learning - Towards a Synthesis.
@inproceedings {dagstuhl05,
    author = { Passerini, A. and Frasconi, P. and De Raedt, L. },
    title = "Kernels for Logic Proof Trees",
    booktitle = "Dagstuhl Seminar 05051: Probabilistic, Logical and Relational Learning - Towards a Synthesis",
    year = "2005",
    note = "(invited)",
}
2004

Learning to discriminate between ligand-bound and disulfide-bound cysteines. A. Passerini and P. Frasconi. In Protein Engineering, Design and Selection 17(4).
@article {proteng04,
    author = { Passerini, A. and Frasconi, P. },
    title = "Learning to discriminate between ligand-bound and disulfide-bound cysteines.",
    journal = "Protein Engineering, Design and Selection",
    year = "2004",
    volume = "17",
    pages = "367--373",
    number = "4",
}
New Results on Error Correcting Output Codes of Kernel Machines A. Passerini, M. Pontil, and P. Frasconi. In IEEE Transactions on Neural Networks 15(1).
@article {tnn04,
    author = { Passerini, A. and Pontil, M. and Frasconi, P. },
    title = "New Results on Error Correcting Output Codes of Kernel Machines",
    journal = "IEEE Transactions on Neural Networks",
    year = "2004",
    volume = "15",
    pages = "45--54",
    number = "1",
}
Kernel Methods, Multiclass Classification and Applications to Computational Molecular Biology A. Passerini. In Ph.D. thesis, Dipartimento di Sistemi e Informatica, Universit\`a degli Studi di Firenze.
@phdthesis {phdthesis,
    author = { Passerini, A. },
    title = "Kernel Methods, Multiclass Classification and Applications to Computational Molecular Biology",
    school = "Dipartimento di Sistemi e Informatica, Universit\`a degli Studi di Firenze",
    year = "2004",
}
2003

A Combination of Support Vector Machines and Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction A. Ceroni, P. Frasconi, A. Passerini, and A. Vullo. In AI*IA 2003: Advances in Artificial Intelligence.
@inproceedings {aiia03,
    author = { Ceroni, A. and Frasconi, P. and Passerini, A. and Vullo, A. },
    title = "A Combination of Support Vector Machines and Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction",
    booktitle = "AI*IA 2003: Advances in Artificial Intelligence",
    year = "2003",
    pages = "142--153",
}
Predicting the Disulfide Bonding State of Cysteines with Combinations of Kernel Machines A. Ceroni, P. Frasconi, A. Passerini, and A. Vullo. In Journal of VLSI Signal Processing 35(3).
@article {vlsi03,
    author = { Ceroni, A. and Frasconi, P. and Passerini, A. and Vullo, A. },
    title = "Predicting the Disulfide Bonding State of Cysteines with Combinations of Kernel Machines",
    journal = "Journal of VLSI Signal Processing",
    year = "2003",
    volume = "35",
    pages = "287--295",
    number = "3",
}
2002

A Two-stage SVM Architecture for Predicting the Disulfide Bonding State of Cysteines P. Frasconi, A. Passerini, and A. Vullo. In Proc. of the IEEE Workshop on Neural Networks for Signal Processing.
@inproceedings {nnsp02,
    author = { Frasconi, P. and Passerini, A. and Vullo, A. },
    title = "A Two-stage {SVM} Architecture for Predicting the Disulfide Bonding State of Cysteines",
    booktitle = "Proc. of the IEEE Workshop on Neural Networks for Signal Processing",
    year = "2002",
}
From Margins to Probabilities in Multiclass Learning Problems A. Passerini, M. Pontil, and P. Frasconi. In Proc. 15th European Conf. on Artificial Intelligence.
@inproceedings {ecai02,
    author = { Passerini, A. and Pontil, M. and Frasconi, P. },
    title = "From Margins to Probabilities in Multiclass Learning Problems",
    booktitle = "Proc. 15th European Conf. on Artificial Intelligence",
    year = "2002",
}
On Tuning Hyper-Parameters of Multiclass Margin Classifiers A. Passerini, M. Pontil, and P. Frasconi. In AI*IA Workshop su Apprendimento Automatico: Metodi e Applicazioni.
@inproceedings {aiia02,
    author = { Passerini, A. and Pontil, M. and Frasconi, P. },
    title = "On Tuning Hyper-Parameters of Multiclass Margin Classifiers",
    booktitle = "AI*IA Workshop su Apprendimento Automatico: Metodi e Applicazioni",
    year = "2002",
}
Predicting the Disulfide Bonding State of Cysteines with Combinations of Kernel Machine A. Ceroni, P. Frasconi, A. Passerini, and A. Vullo. In Primo Workshop Nazionale sulla Bioinformatica dell'AI*IA.
@inproceedings {bits02,
    author = { Ceroni, A. and Frasconi, P. and Passerini, A. and Vullo, A. },
    title = "Predicting the Disulfide Bonding State of Cysteines with Combinations of Kernel Machine",
    booktitle = "Primo Workshop Nazionale sulla Bioinformatica dell'AI*IA",
    year = "2002",
}
2001

Evaluation Methods for Focused Crawling A. Passerini, P. Frasconi, and G. Soda. In Atti del 7 Congresso dell'Associazione Italiana di Intelligenza Artificiale (AI*IA).
@inproceedings {aiia01,
    author = { Passerini, A. and Frasconi, P. and Soda, G. },
    title = "Evaluation Methods for Focused Crawling",
    booktitle = "Atti del 7 Congresso dell'Associazione Italiana di Intelligenza Artificiale (AI*IA)",
    year = "2001",
    address = "Bari, Italia",
}
2000

Tecniche di apprendimento automatico applicate al recupero di informazione da Internet A. Passerini. In Master's thesis, Computer Engineering, Universit\`a degli Studi di Firenze.
@mastersthesis {mastthesis,
    author = { Passerini, A. },
    title = "Tecniche di apprendimento automatico applicate al recupero di informazione da Internet",
    school = "Computer Engineering, Universit\`a degli Studi di Firenze",
    year = "2000",
}