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Andrea Passerini
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- affiliation: University of Trento, Italy
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2020 – today
- 2024
- [j53]Giuseppe Spallitta, Gabriele Masina
, Paolo Morettin, Andrea Passerini
Enhancing SMT-based Weighted Model Integration by structure awareness. Artif. Intell. 328: 104067 (2024) - [j52]Marco Bronzini
Glitter or gold? Deriving structured insights from sustainability reports via large language models. EPJ Data Sci. 13(1): 41 (2024) - [j51]Erich Robbi, Marco Bronzini, Paolo Viappiani, Andrea Passerini:
Personalized bundle recommendation using preference elicitation and the Choquet integral. Frontiers Artif. Intell. 7 (2024) - [j50]Giovanni De Toni, Paolo Viappiani, Stefano Teso, Bruno Lepri, Andrea Passerini:
Personalized Algorithmic Recourse with Preference Elicitation. Trans. Mach. Learn. Res. 2024 (2024) - [c75]Burcu Sayin, Pasquale Minervini, Jacopo Staiano, Andrea Passerini:
Can LLMs Correct Physicians, Yet? Investigating Effective Interaction Methods in the Medical Domain. ClinicalNLP@NAACL 2024: 218-237 - [c74]Seyedehdelaram Esfahani
Preference Elicitation in Interactive and User-centered Algorithmic Recourse: an Initial Exploration. UMAP 2024: 249-254 - [i57]Paolo Morettin, Andrea Passerini, Roberto Sebastiani:
A Unified Framework for Probabilistic Verification of AI Systems via Weighted Model Integration. CoRR abs/2402.04892 (2024) - [i56]Emanuele Marconato, Samuele Bortolotti, Emile van Krieken
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts. CoRR abs/2402.12240 (2024) - [i55]Debodeep Banerjee, Stefano Teso, Burcu Sayin, Andrea Passerini:
Learning To Guide Human Decision Makers With Vision-Language Models. CoRR abs/2403.16501 (2024) - [i54]Burcu Sayin, Pasquale Minervini, Jacopo Staiano, Andrea Passerini:
Can LLMs Correct Physicians, Yet? Investigating Effective Interaction Methods in the Medical Domain. CoRR abs/2403.20288 (2024) - [i53]Marco Bronzini
Unveiling LLMs: The Evolution of Latent Representations in a Temporal Knowledge Graph. CoRR abs/2404.03623 (2024) - [i52]Seyedehdelaram Esfahani, Giovanni De Toni, Bruno Lepri, Andrea Passerini, Katya Tentori, Massimo Zancanaro:
Exploiting Preference Elicitation in Interactive and User-centered Algorithmic Recourse: An Initial Exploration. CoRR abs/2404.05270 (2024) - [i51]Kareem Ahmed, Stefano Teso, Paolo Morettin, Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Yitao Liang, Eric Wang, Kai-Wei Chang, Andrea Passerini, Guy Van den Broeck:
Semantic Loss Functions for Neuro-Symbolic Structured Prediction. CoRR abs/2405.07387 (2024) - [i50]Samuele Bortolotti, Emanuele Marconato, Tommaso Carraro, Paolo Morettin, Emile van Krieken, Antonio Vergari, Stefano Teso, Andrea Passerini:
A Benchmark Suite for Systematically Evaluating Reasoning Shortcuts. CoRR abs/2406.10368 (2024) - [i49]Steve Azzolin, Antonio Longa, Stefano Teso, Andrea Passerini:
Perks and Pitfalls of Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs. CoRR abs/2406.15156 (2024) - [i48]Vincenzo Marco De Luca, Antonio Longa, Andrea Passerini, Pietro Liò:
xAI-Drop: Don't Use What You Cannot Explain. CoRR abs/2407.20067 (2024) - [i47]Giovanni De Toni, Stefano Teso, Bruno Lepri, Andrea Passerini:
Time Can Invalidate Algorithmic Recourse. CoRR abs/2410.08007 (2024) - 2023
- [j49]Emanuele Marconato, Andrea Passerini, Stefano Teso:
Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning. Entropy 25(12): 1574 (2023) - [j48]Nicolò Alessandro Girardini
Adaptation of student behavioural routines during Covid-19: a multimodal approach. EPJ Data Sci. 12(1): 55 (2023) - [j47]Giovanni De Toni
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis. Mach. Learn. 112(4): 1389-1409 (2023) - [j46]Antonio Longa, Veronica Lachi, Gabriele Santin, Monica Bianchini, Bruno Lepri, Pietro Lio, Franco Scarselli, Andrea Passerini:
Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities. Trans. Mach. Learn. Res. 2023 (2023) - [c73]Gianluca Apriceno
A Neuro-Symbolic Approach for Non-Intrusive Load Monitoring. ECAI 2023: 3175-3181 - [c72]Marco Bronzini
Environmentally-Aware Bundle Recommendation Using the Choquet Integral. ECAI 2023: 3182-3189 - [c71]Burcu Sayin, Jie Yang, Andrea Passerini, Fabio Casati
Value-Aware Active Learning. HHAI 2023: 215-223 - [c70]Luca Erculiani, Andrea Bontempelli
Egocentric Hierarchical Visual Semantics. HHAI 2023: 320-329 - [c69]Burcu Sayin
Value-Based Hybrid Intelligence. HHAI 2023: 366-370 - [c68]Steve Azzolin, Antonio Longa, Pietro Barbiero, Pietro Liò, Andrea Passerini:
Global Explainability of GNNs via Logic Combination of Learned Concepts. ICLR 2023 - [c67]Andrea Bontempelli, Stefano Teso, Katya Tentori, Fausto Giunchiglia, Andrea Passerini:
Concept-level Debugging of Part-Prototype Networks. ICLR 2023 - [c66]Emanuele Marconato, Gianpaolo Bontempo, Elisa Ficarra, Simone Calderara, Andrea Passerini, Stefano Teso:
Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal. ICML 2023: 23915-23936 - [c65]Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger:
Meta-Path Learning for Multi-Relational Graph Neural Networks. LoG 2023: 2 - [c64]Raffaele Pojer, Andrea Passerini, Manfred Jaeger:
Generalized Reasoning With Graph Neural Networks by Relational Bayesian Network Encodings. LoG 2023: 16 - [c63]Manfred Jaeger, Antonio Longa, Steve Azzolin, Oliver Schulte, Andrea Passerini:
A Simple Latent Variable Model for Graph Learning and Inference. LoG 2023: 26 - [c62]Emanuele Marconato, Stefano Teso, Andrea Passerini:
Neuro-Symbolic Reasoning Shortcuts: Mitigation Strategies and their Limitations. NeSy 2023: 162-166 - [c61]Emanuele Marconato, Andrea Passerini, Stefano Teso:
GlanceNets: Interpretable, Leak-proof Concept-based Models. NeSy 2023: 410 - [c60]Emanuele Marconato, Stefano Teso, Antonio Vergari, Andrea Passerini:
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts. NeurIPS 2023 - [p4]Kareem Ahmed, Stefano Teso, Paolo Morettin, Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Yitao Liang, Eric Wang, Kai-Wei Chang, Andrea Passerini, Guy Van den Broeck:
Semantic Loss Functions for Neuro-Symbolic Structured Prediction. Compendium of Neurosymbolic Artificial Intelligence 2023: 485-505 - [i46]Antonio Longa, Veronica Lachi, Gabriele Santin, Monica Bianchini, Bruno Lepri, Pietro Liò, Franco Scarselli, Andrea Passerini:
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities. CoRR abs/2302.01018 (2023) - [i45]Emanuele Marconato, Gianpaolo Bontempo
Neuro Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal. CoRR abs/2302.01242 (2023) - [i44]Giuseppe Spallitta, Gabriele Masina, Paolo Morettin, Andrea Passerini, Roberto Sebastiani:
Enhancing SMT-based Weighted Model Integration by Structure Awareness. CoRR abs/2302.06188 (2023) - [i43]Emanuele Marconato, Stefano Teso, Andrea Passerini:
Neuro-Symbolic Reasoning Shortcuts: Mitigation Strategies and their Limitations. CoRR abs/2303.12578 (2023) - [i42]Samy Badreddine, Gianluca Apriceno, Andrea Passerini, Luciano Serafini:
Interval Logic Tensor Networks. CoRR abs/2303.17892 (2023) - [i41]Luca Erculiani, Andrea Bontempelli, Andrea Passerini, Fausto Giunchiglia:
Egocentric Hierarchical Visual Semantics. CoRR abs/2305.05422 (2023) - [i40]Emanuele Marconato, Stefano Teso, Antonio Vergari
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts. CoRR abs/2305.19951 (2023) - [i39]Nicolò Alessandro Girardini, Simone Centellegher, Andrea Passerini, Ivano Bison, Fausto Giunchiglia, Bruno Lepri:
Adaptation of Student Behavioural Routines during COVID-19: A Multimodal Approach. CoRR abs/2306.08561 (2023) - [i38]Debodeep Banerjee, Stefano Teso, Andrea Passerini:
Learning to Guide Human Experts via Personalized Large Language Models. CoRR abs/2308.06039 (2023) - [i37]Emanuele Marconato, Andrea Passerini, Stefano Teso:
Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning. CoRR abs/2309.07742 (2023) - [i36]Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger:
Meta-Path Learning for Multi-relational Graph Neural Networks. CoRR abs/2309.17113 (2023) - [i35]Marco Bronzini, Carlo Nicolini, Bruno Lepri, Andrea Passerini, Jacopo Staiano:
Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models. CoRR abs/2310.05628 (2023) - 2022
- [j45]Wanyi Zhang
Skeptical Learning - An Algorithm and a Platform for Dealing with Mislabeling in Personal Context Recognition. Algorithms 15(4): 109 (2022) - [j44]Antonio Longa
An efficient procedure for mining egocentric temporal motifs. Data Min. Knowl. Discov. 36(1): 355-378 (2022) - [j43]Andrea Bontempelli
Human-in-the-loop handling of knowledge drift. Data Min. Knowl. Discov. 36(5): 1865-1884 (2022) - [c59]Emanuele Marconato
Catastrophic Forgetting in Continual Concept Bottleneck Models. ICIAP Workshops 2022: 539-547 - [c58]Alessia Bertugli, Stefano Vincenzi, Simone Calderara, Andrea Passerini:
Generalising via Meta-examples for Continual Learning in the Wild. LOD (1) 2022: 414-429 - [c57]Emanuele Marconato, Andrea Passerini, Stefano Teso:
GlanceNets: Interpretable, Leak-proof Concept-based Models. NeurIPS 2022 - [c56]Gianluca Apriceno
A Neuro-Symbolic Approach for Real-World Event Recognition from Weak Supervision. TIME 2022: 12:1-12:19 - [c55]Giuseppe Spallitta, Gabriele Masina, Paolo Morettin, Andrea Passerini, Roberto Sebastiani:
SMT-based weighted model integration with structure awareness. UAI 2022: 1876-1885 - [e3]Andrea Passerini, Thomas Schiex:
PAIS 2022 - 11th Conference on Prestigious Applications of Artificial Intelligence, 25 July 2022, Vienna, Austria (co-located with IJCAI-ECAI 2022). Frontiers in Artificial Intelligence and Applications 351, IOS Press 2022, ISBN 978-1-64368-294-5 [contents] - [i34]Giovanni De Toni, Bruno Lepri, Andrea Passerini:
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis. CoRR abs/2201.07135 (2022) - [i33]Antonio Longa, Giulia Cencetti, Sune Lehmann, Andrea Passerini, Bruno Lepri:
Neighbourhood matching creates realistic surrogate temporal networks. CoRR abs/2205.08820 (2022) - [i32]Andrea Bontempelli
Lifelong Personal Context Recognition. CoRR abs/2205.10123 (2022) - [i31]Stefano Teso
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens. CoRR abs/2205.10157 (2022) - [i30]Giovanni De Toni, Paolo Viappiani, Bruno Lepri, Andrea Passerini:
Generating personalized counterfactual interventions for algorithmic recourse by eliciting user preferences. CoRR abs/2205.13743 (2022) - [i29]Emanuele Marconato, Andrea Passerini, Stefano Teso
GlanceNets: Interpretabile, Leak-proof Concept-based Models. CoRR abs/2205.15612 (2022) - [i28]Andrea Bontempelli, Stefano Teso
Concept-level Debugging of Part-Prototype Networks. CoRR abs/2205.15769 (2022) - [i27]Giuseppe Spallitta, Gabriele Masina
SMT-based Weighted Model Integration with Structure Awareness. CoRR abs/2206.13856 (2022) - [i26]Burcu Sayin, Fabio Casati, Andrea Passerini, Jie Yang, Xinyue Chen:
Rethinking and Recomputing the Value of ML Models. CoRR abs/2209.15157 (2022) - [i25]Steve Azzolin, Antonio Longa, Pietro Barbiero, Pietro Liò, Andrea Passerini:
Global Explainability of GNNs via Logic Combination of Learned Concepts. CoRR abs/2210.07147 (2022) - [i24]Antonio Longa, Steve Azzolin, Gabriele Santin, Giulia Cencetti, Pietro Liò, Bruno Lepri, Andrea Passerini:
Explaining the Explainers in Graph Neural Networks: a Comparative Study. CoRR abs/2210.15304 (2022) - 2021
- [j42]Paolo Campigotto
Learning Modulo Theories for constructive preference elicitation. Artif. Intell. 295: 103454 (2021) - [j41]Burcu Sayin
A review and experimental analysis of active learning over crowdsourced data. Artif. Intell. Rev. 54(7): 5283-5305 (2021) - [j40]Wanyi Zhang
Putting human behavior predictability in context. EPJ Data Sci. 10(1): 42 (2021) - [j39]Mirco Nanni, Gennady L. Andrienko, Albert-László Barabási, Chiara Boldrini, Francesco Bonchi, Ciro Cattuto, Francesca Chiaromonte, Giovanni Comandè, Marco Conti, Mark Coté, Frank Dignum, Virginia Dignum, Josep Domingo-Ferrer, Paolo Ferragina, Fosca Giannotti, Riccardo Guidotti, Dirk Helbing, Kimmo Kaski, János Kertész, Sune Lehmann, Bruno Lepri, Paul Lukowicz, Stan Matwin, David Megías Jiménez, Anna Monreale, Katharina Morik, Nuria Oliver, Andrea Passarella, Andrea Passerini, Dino Pedreschi
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment. Ethics Inf. Technol. 23(S1): 1-6 (2021) - [j38]Fausto Giunchiglia
Towards Visual Semantics. SN Comput. Sci. 2(6): 446 (2021) - [c54]Giovanni Pellegrini, Alessandro Tibo
Learning Aggregation Functions. IJCAI 2021: 2892-2898 - [c53]Paolo Morettin, Pedro Zuidberg Dos Martires, Samuel Kolb, Andrea Passerini:
Hybrid Probabilistic Inference with Logical and Algebraic Constraints: a Survey. IJCAI 2021: 4533-4542 - [c52]Paolo Morettin, Andrea Passerini, Stefano Teso:
Co-creating Platformer Levels with Constrained Adversarial Networks. IUI Workshops 2021 - [c51]Paolo Dragone, Stefano Teso, Andrea Passerini:
Neuro-Symbolic Constraint Programming for Structured Prediction. NeSy 2021: 6-14 - [c50]Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini:
Interactive Label Cleaning with Example-based Explanations. NeurIPS 2021: 12966-12977 - [c49]Gianluca Apriceno
A Neuro-Symbolic Approach to Structured Event Recognition. TIME 2021: 11:1-11:14 - [i23]Alessia Bertugli, Stefano Vincenzi, Simone Calderara, Andrea Passerini:
Generalising via Meta-Examples for Continual Learning in the Wild. CoRR abs/2101.12081 (2021) - [i22]Andrea Bontempelli
Human-in-the-loop Handling of Knowledge Drift. CoRR abs/2103.14874 (2021) - [i21]Paolo Dragone, Stefano Teso, Andrea Passerini:
Neuro-Symbolic Constraint Programming for Structured Prediction. CoRR abs/2103.17232 (2021) - [i20]Fausto Giunchiglia, Luca Erculiani, Andrea Passerini:
Towards Visual Semantics. CoRR abs/2104.12379 (2021) - [i19]Stefano Teso, Andrea Bontempelli
Interactive Label Cleaning with Example-based Explanations. CoRR abs/2106.03922 (2021) - [i18]Giovanni De Toni, Luca Erculiani, Andrea Passerini:
Learning compositional programs with arguments and sampling. CoRR abs/2109.00619 (2021) - [i17]Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini, Stefano Teso:
Toward a Unified Framework for Debugging Gray-box Models. CoRR abs/2109.11160 (2021) - [i16]Antonio Longa, Giulia Cencetti, Bruno Lepri, Andrea Passerini:
An Efficient Procedure for Mining Egocentric Temporal Motifs. CoRR abs/2110.01391 (2021) - [i15]Burcu Sayin, Jie Yang, Andrea Passerini, Fabio Casati:
The Science of Rejection: A Research Area for Human Computation. CoRR abs/2111.06736 (2021) - 2020
- [j37]Wanyi Zhang
Dealing with Mislabeling via Interactive Machine Learning. Künstliche Intell. 34(2): 271-278 (2020) - [j36]Mirco Nanni, Gennady L. Andrienko, Albert-László Barabási, Chiara Boldrini, Francesco Bonchi, Ciro Cattuto, Francesca Chiaromonte, Giovanni Comandè, Marco Conti, Mark Coté, Frank Dignum, Virginia Dignum, Josep Domingo-Ferrer, Paolo Ferragina, Fosca Giannotti, Riccardo Guidotti, Dirk Helbing, Kimmo Kaski, János Kertész, Sune Lehmann, Bruno Lepri, Paul Lukowicz, Stan Matwin, David Megías, Anna Monreale, Katharina Morik, Nuria Oliver, Andrea Passarella, Andrea Passerini, Dino Pedreschi, Alex Pentland, Fabio Pianesi, Francesca Pratesi, Salvatore Rinzivillo, Salvatore Ruggieri, Arno Siebes, Vicenç Torra
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment. Trans. Data Priv. 13(1): 61-66 (2020) - [j35]Subhankar Roy
Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound. IEEE Trans. Medical Imaging 39(8): 2676-2687 (2020) - [c48]Paolo Morettin, Samuel Kolb, Stefano Teso, Andrea Passerini:
Learning Weighted Model Integration Distributions. AAAI 2020: 5224-5231 - [c47]Luca Erculiani, Fausto Giunchiglia, Andrea Passerini:
Continual Egocentric Object Recognition. ECAI 2020: 1127-1134 - [c46]Andrea Bontempelli
Learning in the Wild with Incremental Skeptical Gaussian Processes. IJCAI 2020: 2886-2892 - [c45]Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, Andrea Passerini:
Efficient Generation of Structured Objects with Constrained Adversarial Networks. NeurIPS 2020 - [i14]Mirco Nanni, Gennady L. Andrienko, Albert-László Barabási, Chiara Boldrini, Francesco Bonchi, Ciro Cattuto, Francesca Chiaromonte, Giovanni Comandè, Marco Conti, Mark Coté, Frank Dignum, Virginia Dignum, Josep Domingo-Ferrer, Fosca Giannotti, Riccardo Guidotti, Dirk Helbing, János Kertész, Sune Lehmann, Bruno Lepri, Paul Lukowicz, Anna Monreale, Katharina Morik, Nuria Oliver, Andrea Passarella, Andrea Passerini, Dino Pedreschi, Alex Pentland, Francesca Pratesi, Salvatore Rinzivillo, Salvatore Ruggieri, Arno Siebes, Roberto Trasarti, Jeroen van den Hoven, Alessandro Vespignani:
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment. CoRR abs/2004.05222 (2020) - [i13]Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, Andrea Passerini:
Efficient Generation of Structured Objects with Constrained Adversarial Networks. CoRR abs/2007.13197 (2020) - [i12]Alessia Bertugli, Stefano Vincenzi, Simone Calderara, Andrea Passerini:
Few-Shot Unsupervised Continual Learning through Meta-Examples. CoRR abs/2009.08107 (2020) - [i11]Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia, Andrea Passerini:
Learning in the Wild with Incremental Skeptical Gaussian Processes. CoRR abs/2011.00928 (2020) - [i10]Giovanni Pellegrini, Alessandro Tibo, Paolo Frasconi, Andrea Passerini, Manfred Jaeger:
Learning Aggregation Functions. CoRR abs/2012.08482 (2020)
2010 – 2019
- 2019
- [j34]Paolo Morettin
Advanced SMT techniques for weighted model integration. Artif. Intell. 275: 1-27 (2019) - [j33]Stefano Teso
Combining learning and constraints for genome-wide protein annotation. BMC Bioinform. 20(1): 338:1-338:14 (2019) - [j32]Manfred Jaeger
Counts-of-counts similarity for prediction and search in relational data. Data Min. Knowl. Discov. 33(5): 1254-1297 (2019) - [j31]Chiara Ghidini, Bernardo Magnini, Andrea Passerini
Special issue: Selected and revised papers from the 17th International Conference of the Italian Association for Artificial Intelligence. Intelligenza Artificiale 13(1): 1-4 (2019) - [j30]Mattia Zeni
Fixing Mislabeling by Human Annotators Leveraging Conflict Resolution and Prior Knowledge. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(1): 32:1-32:23 (2019) - [j29]Leonardo Maccari
A Big Data and machine learning approach for network monitoring and security. Secur. Priv. 2(1) (2019) - [c44]Samuel Kolb, Paolo Morettin
The pywmi Framework and Toolbox for Probabilistic Inference using Weighted Model Integration. IJCAI 2019: 6530-6532 - [i9]Luca Erculiani, Fausto Giunchiglia, Andrea Passerini:
Continual egocentric object recognition. CoRR abs/1912.05029 (2019) - 2018
- [c43]Paolo Dragone, Stefano Teso, Mohit Kumar, Andrea Passerini:
Decomposition Strategies for Constructive Preference Elicitation. AAAI 2018: 2934-2942 - [c42]Paolo Dragone, Stefano Teso, Andrea Passerini:
Constructive Preference Elicitation Over Hybrid Combinatorial Spaces. AAAI 2018: 2943-2950 - [c41]Luc De Raedt, Andrea Passerini, Stefano Teso:
Learning Constraints From Examples. AAAI 2018: 7965-7970 - [c40]Samuel Kolb, Stefano Teso
Learning SMT(LRA) Constraints using SMT Solvers. IJCAI 2018: 2333-2340 - [c39]Paolo Dragone, Stefano Teso
Pyconstruct: Constraint Programming Meets Structured Prediction. IJCAI 2018: 5823-5825 - [c38]Luca Erculiani, Paolo Dragone, Stefano Teso
Automating Layout Synthesis with Constructive Preference Elicitation. ECML/PKDD (3) 2018: 254-270 - [c37]Paolo Dragone, Giovanni Pellegrini, Michele Vescovi, Katya Tentori, Andrea Passerini
No more ready-made deals: constructive recommendation for telco service bundling. RecSys 2018: 163-171 - [e2]Chiara Ghidini, Bernardo Magnini, Andrea Passerini, Paolo Traverso:
AI*IA 2018 - Advances in Artificial Intelligence - XVIIth International Conference of the Italian Association for Artificial Intelligence, Trento, Italy, November 20-23, 2018, Proceedings. Lecture Notes in Computer Science 11298, Springer 2018, ISBN 978-3-030-03839-7 [contents] - 2017
- [j28]Andrea Passerini
Introduction to the special issue on Combining Constraint Solving with Mining and Learning. Artif. Intell. 244: 1-5 (2017) - [j27]Stefano Teso
Structured learning modulo theories. Artif. Intell. 244: 166-187 (2017) - [j26]Paolo Dragone, Stefano Teso
Constructive Preference Elicitation. Frontiers Robotics AI 4: 71 (2017) - [c36]Stefano Teso, Paolo Dragone, Andrea Passerini:
Coactive Critiquing: Elicitation of Preferences and Features. AAAI 2017: 2639-2645 - [c35]Stefano Teso
Constructive Preference Elicitation for Multiple Users with Setwise Max-margin. ADT 2017: 3-17 - [c34]Paolo Morettin
Efficient Weighted Model Integration via SMT-Based Predicate Abstraction. IJCAI 2017: 720-728 - [i8]Paolo Dragone, Stefano Teso, Andrea Passerini:
Constructive Preference Elicitation over Hybrid Combinatorial Spaces. CoRR abs/1711.07875 (2017) - [i7]Paolo Dragone, Stefano Teso, Mohit Kumar, Andrea Passerini:
Decomposition Strategies for Constructive Preference Elicitation. CoRR abs/1711.08247 (2017) - 2016
- [j25]Gianluca Corrado, Toma Tebaldi
RNAcommender: genome-wide recommendation of RNA-protein interactions. Bioinform. 32(23): 3627-3634 (2016) - [j24]Thomas Gärtner, Mirco Nanni, Andrea Passerini
Guest editors' introduction to the EcmlPkdd 2016 journal track special issue of Machine Learning. Data Min. Knowl. Discov. 30(5): 995-997 (2016) - [j23]Thomas Gärtner, Mirco Nanni, Andrea Passerini
Guest editors' introduction to the EcmlPkdd 2016 journal track special issue of Machine Learning. Mach. Learn. 104(2-3): 149-150 (2016) - [c33]Vaishak Belle, Guy Van den Broeck, Andrea Passerini:
Component Caching in Hybrid Domains with Piecewise Polynomial Densities. AAAI 2016: 3369-3375 - [c32]Emanuele Sansone, Andrea Passerini
Classtering: Joint Classification and Clustering with Mixture of Factor Analysers. ECAI 2016: 1089-1095 - [c31]Stefano Teso, Andrea Passerini, Paolo Viappiani:
Constructive Preference Elicitation by Setwise Max-Margin Learning. IJCAI 2016: 2067-2073 - [c30]Vaishak Belle, Guy Van den Broeck, Andrea Passerini:
Hashing-Based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report. IJCAI 2016: 4115-4119 - [p3]Andrea Passerini
Learning Modulo Theories. Data Mining and Constraint Programming 2016: 113-146 - [i6]Stefano Teso, Andrea Passerini, Paolo Viappiani:
Constructive Preference Elicitation by Setwise Max-margin Learning. CoRR abs/1604.06020 (2016) - [i5]Stefano Teso, Paolo Dragone, Andrea Passerini:
Coactive Critiquing: Elicitation of Preferences and Features. CoRR abs/1612.01941 (2016) - 2015
- [c29]Daniil Mirylenka, Andrea Passerini, Luciano Serafini:
Bootstrapping Domain Ontologies from Wikipedia: A Uniform Approach. IJCAI 2015: 1464-1470 - [c28]Vaishak Belle, Andrea Passerini, Guy Van den Broeck:
Probabilistic Inference in Hybrid Domains by Weighted Model Integration. IJCAI 2015: 2770-2776 - [c27]Vaishak Belle, Guy Van den Broeck, Andrea Passerini:
Hashing-Based Approximate Probabilistic Inference in Hybrid Domains. UAI 2015: 141-150 - [e1]Dietmar Jannach, Jérôme Mengin, Bamshad Mobasher, Andrea Passerini, Paolo Viappiani:
Proceedings of the IJCAI 2015 Joint Workshop on Constraints and Preferences for Configuration and Recommendation and Intelligent Techniques for Web Personalization co-located with the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina, July 27, 2015. CEUR Workshop Proceedings 1440, CEUR-WS.org 2015 [contents] - [i4]Paolo Campigotto, Roberto Battiti, Andrea Passerini:
Learning Modulo Theories for preference elicitation in hybrid domains. CoRR abs/1508.04261 (2015) - 2014
- [j22]Stefano Teso
Joint probabilistic-logical refinement of multiple protein feature predictors. BMC Bioinform. 15: 16 (2014) - [j21]Claudio Saccà, Stefano Teso
Improved multi-level protein¿protein interaction prediction with semantic-based regularization. BMC Bioinform. 15: 103 (2014) - [j20]Elisa Cilia, Stefano Teso
Predicting virus mutations through statistical relational learning. BMC Bioinform. 15: 309 (2014) - [j19]Umut Avci, Andrea Passerini
Improving Activity Recognitionby Segmental Pattern Mining. IEEE Trans. Knowl. Data Eng. 26(4): 889-902 (2014) - [j18]Paolo Campigotto, Andrea Passerini
Active Learning of Pareto Fronts. IEEE Trans. Neural Networks Learn. Syst. 25(3): 506-519 (2014) - [i3]Stefano Teso, Roberto Sebastiani, Andrea Passerini:
Hybrid SRL with Optimization Modulo Theories. CoRR abs/1402.4354 (2014) - [i2]Stefano Teso, Roberto Sebastiani, Andrea Passerini:
Structured Learning Modulo Theories. CoRR abs/1405.1675 (2014) - 2013
- [j17]Manfred Jaeger
Type Extension Trees for feature construction and learning in relational domains. Artif. Intell. 204: 30-55 (2013) - [c26]Daniil Mirylenka, Andrea Passerini
Navigating the topical structure of academic search results via the Wikipedia category network. CIKM 2013: 891-896 - [c25]Umut Avci, Andrea Passerini
A Fully Unsupervised Approach to Activity Discovery. HBU 2013: 77-88 - [c24]Daniil Mirylenka, Andrea Passerini
ScienScan - An Efficient Visualization and Browsing Tool for Academic Search. ECML/PKDD (3) 2013: 667-671 - [c23]Stefano Teso
Ego-centric Graphlets for Personality and Affective States Recognition. SocialCom 2013: 874-877 - [p2]Andrea Passerini
Kernel Methods for Structured Data. Handbook on Neural Information Processing 2013: 283-333 - 2012
- [j16]Andrea Passerini
Predicting Metal-Binding Sites from Protein Sequence. IEEE ACM Trans. Comput. Biol. Bioinform. 9(1): 203-213 (2012) - [c22]Anna Corazza
Using Machine Learning and Information Retrieval Techniques to Improve Software Maintainability. EternalS@ECAI 2012: 117-134 - [c21]Elisa Cilia, Stefano Teso, Sergio Ammendola, Tom Lenaerts, Andrea Passerini:
Predicting virus mutations through relational learning. AIMM 2012 - [c20]Umut Avci, Andrea Passerini
Improving activity recognition by segmental pattern mining. PerCom Workshops 2012: 709-714 - [c19]Marco Lippi
Metal Binding in Proteins: Machine Learning Complements X-Ray Absorption Spectroscopy. ECML/PKDD (2) 2012: 854-857 - 2011
- [j15]Elisa Cilia, Niels Landwehr, Andrea Passerini
Relational Feature Mining with Hierarchical Multitask kFOIL. Fundam. Informaticae 113(2): 151-177 (2011) - [j14]Marco Lippi
Relational information gain. Mach. Learn. 83(2): 219-239 (2011) - [j13]Andrea Passerini
MetalDetector v2.0: predicting the geometry of metal binding sites from protein sequence. Nucleic Acids Res. 39(Web-Server-Issue): 288-292 (2011) - [c18]Paolo Campigotto, Andrea Passerini
Active Learning of Combinatorial Features for Interactive Optimization. LION 2011: 336-350 - 2010
- [j12]Elisa Cilia, Andrea Passerini
Automatic prediction of catalytic residues by modeling residue structural neighborhood. BMC Bioinform. 11: 115 (2010) - [j11]Niels Landwehr, Andrea Passerini
Fast learning of relational kernels. Mach. Learn. 78(3): 305-342 (2010) - [j10]Roberto Battiti
Brain-Computer Evolutionary Multiobjective Optimization: A Genetic Algorithm Adapting to the Decision Maker. IEEE Trans. Evol. Comput. 14(5): 671-687 (2010) - [c17]Franco Mascia, Elisa Cilia, Mauro Brunato, Andrea Passerini:
Predicting Structural and Functional Sites in Proteins by Searching for Maximum-weight Cliques. AAAI 2010: 1274-1279 - [c16]Carlo Nicolini, Bruno Lepri, Stefano Teso
From On-Going to Complete Activity Recognition Exploiting Related Activities. HBU 2010: 26-37 - [c15]Paolo Campigotto, Andrea Passerini
Adapting to a Realistic Decision Maker: Experiments towards a Reactive Multi-objective Optimizer. LION 2010: 338-341 - [c14]Stefano Teso
An On/Off Lattice Approach to Protein Structure Prediction from Contact Maps. PRIB 2010: 368-379
2000 – 2009
- 2008
- [j9]Fabrizio Costa
A semiparametric generative model for efficient structured-output supervised learning. Ann. Math. Artif. Intell. 54(1-3): 207-222 (2008) - [j8]Marco Lippi
MetalDetector: a web server for predicting metal-binding sites and disulfide bridges in proteins from sequence. Bioinform. 24(18): 2094-2095 (2008) - [j7]Marc Vincent, Andrea Passerini
A simplified approach to disulfide connectivity prediction from protein sequences. BMC Bioinform. 9 (2008) - [c13]Alessandro Vullo, Andrea Passerini
On the Convergence of Protein Structure and Dynamics. Statistical Learning Studies of Pseudo Folding Pathways. EvoBIO 2008: 200-211 - [c12]Paolo Frasconi, Manfred Jaeger
Feature Discovery with Type Extension Trees. ILP 2008: 122-139 - [c11]Paolo Frasconi, Andrea Passerini:
Predicting the Geometry of Metal Binding Sites from Protein Sequence. NIPS 2008: 465-472 - [p1]Paolo Frasconi, Andrea Passerini
Learning with Kernels and Logical Representations. Probabilistic Inductive Logic Programming 2008: 56-91 - 2007
- [j6]Enrico Francesconi, Andrea Passerini
Automatic Classification of Provisions in Legislative Texts. Artif. Intell. Law 15(1): 1-17 (2007) - [j5]Andrea Passerini
Predicting zinc binding at the proteome level. BMC Bioinform. 8 (2007) - 2006
- [j4]Andrea Passerini, Paolo Frasconi, Luc De Raedt:
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting. J. Mach. Learn. Res. 7: 307-342 (2006) - [j3]Alessio Ceroni, Andrea Passerini
DISULFIND: a disulfide bonding state and cysteine connectivity prediction server. Nucleic Acids Res. 34(Web-Server-Issue): 177-181 (2006) - [c10]Niels Landwehr, Andrea Passerini, Luc De Raedt, Paolo Frasconi:
kFOIL: Learning Simple Relational Kernels. AAAI 2006: 389-394 - [c9]Fabrizio Costa, Sauro Menchetti, Alessio Ceroni, Andrea Passerini, Paolo Frasconi:
Decomposition Kernels for Natural Language Processing. Learning Structured Information@EACL 2006 - [c8]Sauro Menchetti, Andrea Passerini
Improving Prediction of Zinc Binding Sites by Modeling the Linkage Between Residues Close in Sequence. RECOMB 2006: 309-320 - 2005
- [c7]Andrea Passerini, Paolo Frasconi, Luc De Raedt:
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting. AAIP 2005: 37-48 - [c6]Carlo Biagioli, Enrico Francesconi, Andrea Passerini
Automatic semantics extraction in law documents. ICAIL 2005: 133-140 - [c5]Andrea Passerini, Paolo Frasconi:
Kernels on Prolog Ground Terms. IJCAI 2005: 1626-1627 - [i1]Andrea Passerini, Paolo Frasconi, Luc De Raedt:
Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting. Probabilistic, Logical and Relational Learning 2005 - 2004
- [b1]Andrea Passerini:
Kernel methods, multiclass classification and applications to computational molecular biology. University of Florence, Italy, 2004 - [j2]Andrea Passerini
New results on error correcting output codes of kernel machines. IEEE Trans. Neural Networks 15(1): 45-54 (2004) - 2003
- [j1]Alessio Ceroni, Paolo Frasconi, Andrea Passerini
Predicting the Disulfide Bonding State of Cysteines with Combinations of Kernel Machines. J. VLSI Signal Process. 35(3): 287-295 (2003) - [c4]Alessio Ceroni, Paolo Frasconi, Andrea Passerini, Alessandro Vullo:
A Combination of Support Vector Machines and Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction. AI*IA 2003: 142-153 - 2002
- [c3]Andrea Passerini, Massimiliano Pontil, Paolo Frasconi:
From Margins to Probabilities in Multiclass Learning Problems. ECAI 2002: 400-404 - [c2]Paolo Frasconi, Andrea Passerini
A two-stage SVM architecture for predicting the disulfide bonding state of cysteines. NNSP 2002: 25-34 - 2001
- [c1]Andrea Passerini, Paolo Frasconi, Giovanni Soda:
Evaluation Methods for Focused Crawling. AI*IA 2001: 33-39
Coauthor Index
[j50] [i56] [i55] [i51] [i50] [i49] [i47] [j49] [c67] [c66] [c62] [c61] [c60] [p4] [i45] [i43] [i40] [i38] [i37] [j43] [c59] [c57] [i32] [i31] [i29] [i28] [j42] [j40] [c52] [c51] [c50] [i22] [i21] [i19] [i17] [c48] [c46] [c45] [i13] [i11] [j33] [c43] [c42] [c41] [c40] [c39] [c38] [j27] [j26] [c36] [c35] [i8] [i7] [c31] [i6] [i5] [j22] [j21] [j20] [i3] [i2] [c23] [c21] [c16] [c14]
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