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2020 – today
- 2025
[j29]Luca Bergamin
, Mirko Polato, Fabio Aiolli:
Improving rule-based classifiers by Bayes point aggregation. Neurocomputing 613: 128699 (2025)- 2024
- [c68]Tommaso Carraro, Alessandro Daniele, Fabio Aiolli, Luciano Serafini:
Mitigating Data Sparsity via Neuro-Symbolic Knowledge Transfer. ECIR (3) 2024: 226-242 - [i9]Tommaso Carraro, Luciano Serafini, Fabio Aiolli:
LTNtorch: PyTorch Implementation of Logic Tensor Networks. CoRR abs/2409.16045 (2024) - 2023
- [j28]Alvise De Biasio, Andrea Montagna, Fabio Aiolli, Nicolò Navarin:
A systematic review of value-aware recommender systems. Expert Syst. Appl. 226: 120131 (2023) - [c67]Andrea Montagna, Alvise De Biasio, Nicolò Navarin, Fabio Aiolli:
Graph-based Explainable Recommendation Systems: Are We Rigorously Evaluating Explanations? HCAI4U@CHItaly 2023 - [c66]Tommaso Carraro, Alessandro Daniele, Fabio Aiolli, Luciano Serafini:
Mitigating Data Sparsity via Neuro-Symbolic Knowledge Transfer. KaRS@RecSys 2023: 69-79 - 2022
- [j27]Ivano Lauriola, Alberto Lavelli, Fabio Aiolli:
An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools. Neurocomputing 470: 443-456 (2022) - [j26]Mirko Polato, Guglielmo Faggioli, Fabio Aiolli:
PRL: A game theoretic large margin method for interpretable feature learning. Neurocomputing 479: 106-120 (2022) - [j25]Fabio Aiolli, Mauro Conti, Stjepan Picek, Mirko Polato:
On the feasibility of crawling-based attacks against recommender systems. J. Comput. Secur. 30(4): 599-621 (2022) - [c65]Tommaso Carraro, Alessandro Daniele, Fabio Aiolli, Luciano Serafini:
Logic Tensor Networks for Top-N Recommendation. AI*IA 2022: 110-123 - [c64]Mohammadmahdi Ghahramani, Fabio Aiolli:
Price direction prediction in financial markets, using Random Forest and Adaboost. ESANN 2022 - [c63]Mirko Polato, Fabio Aiolli, Luca Bergamin, Tommaso Carraro:
Bayes Point Rule Set Learning. ESANN 2022 - [c62]Tommaso Carraro, Mirko Polato, Luca Bergamin, Fabio Aiolli:
Conditioned Variational Autoencoder for Top-N Item Recommendation. ICANN (2) 2022: 785-796 - [c61]Luca Bergamin, Tommaso Carraro, Mirko Polato, Fabio Aiolli:
Novel Applications for VAE-based Anomaly Detection Systems. IJCNN 2022: 1-8 - [c60]Tommaso Carraro, Alessandro Daniele, Fabio Aiolli, Luciano Serafini:
Logic Tensor Networks for Top-N Recommendation. NeSy 2022: 1-14 - [i8]Fabio Aiolli, Luca Bergamin, Tommaso Carraro, Mirko Polato:
Bayes Point Rule Set Learning. CoRR abs/2204.05251 (2022) - [i7]Luca Bergamin, Tommaso Carraro, Mirko Polato, Fabio Aiolli:
Novel Applications for VAE-based Anomaly Detection Systems. CoRR abs/2204.12577 (2022) - 2021
- [j24]Ivano Lauriola, Fabio Aiolli, Alberto Lavelli, Fabio Rinaldi:
Learning adaptive representations for entity recognition in the biomedical domain. J. Biomed. Semant. 12(1): 10 (2021) - [j23]Mirko Polato, Fabio Aiolli:
Propositional Kernels. Entropy 23(8): 1020 (2021) - [c59]Mirko Polato, Alberto Gallinaro, Fabio Aiolli:
Privacy-Preserving Kernel Computation For Vertically Partitioned Data. ESANN 2021 - [c58]Ivano Lauriola, Alberto Lavelli, Alessandro Moschitti, Fabio Aiolli:
Exploring the structure of BERT through Kernel Learning. IJCNN 2021: 1-9 - 2020
- [j22]Ivano Lauriola, Claudio Gallicchio, Fabio Aiolli:
Enhancing deep neural networks via multiple kernel learning. Pattern Recognit. 101: 107194 (2020) - [j21]Ivano Lauriola, Mirko Polato, Fabio Aiolli:
Learning deep kernels in the space of monotone conjunctive polynomials. Pattern Recognit. Lett. 140: 200-206 (2020) - [c57]Pasquale Capuozzo, Ivano Lauriola, Carlo Strapparava, Fabio Aiolli, Giuseppe Sartori:
Automatic Detection of Cross-language Verbal Deception. CogSci 2020 - [c56]Ivano Lauriola, Alberto Lavelli, Fabio Aiolli:
Language processing in the era of deep learning. ESANN 2020: 597-606 - [c55]Ivano Lauriola, Stefano Campese, Alberto Lavelli, Fabio Rinaldi, Fabio Aiolli:
Exploring the feature space of character-level embeddings. ESANN 2020: 637-642 - [c54]Fabio Aiolli, Mauro Conti, Stjepan Picek, Mirko Polato:
Big Enough to Care Not Enough to Scare! Crawling to Attack Recommender Systems. ESORICS (2) 2020: 165-184 - [c53]Ivano Lauriola, Fabio Aiolli:
Monotone Deep Spectrum Kernels. ICANN (1) 2020: 207-219 - [c52]Pasquale Capuozzo, Ivano Lauriola, Carlo Strapparava, Fabio Aiolli, Giuseppe Sartori:
DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text. LREC 2020: 1423-1430 - [c51]Guglielmo Faggioli, Mirko Polato, Fabio Aiolli:
Recency Aware Collaborative Filtering for Next Basket Recommendation. UMAP 2020: 80-87 - [c50]Tommaso Carraro, Mirko Polato, Fabio Aiolli:
A Look Inside the Black-Box: Towards the Interpretability of Conditioned Variational Autoencoder for Collaborative Filtering. UMAP (Adjunct Publication) 2020: 233-236 - [i6]Mirko Polato, Tommaso Carraro, Fabio Aiolli:
Conditioned Variational Autoencoder for top-N item recommendation. CoRR abs/2004.11141 (2020) - [i5]Ivano Lauriola, Fabio Aiolli:
MKLpy: a python-based framework for Multiple Kernel Learning. CoRR abs/2007.09982 (2020)
2010 – 2019
- 2019
- [j20]Mirko Polato, Fabio Aiolli:
Boolean kernels for rule based interpretation of support vector machines. Neurocomputing 342: 113-124 (2019) - [c49]Giovanni Da San Martino, Alessandro Sperduti, Fabio Aiolli, Alessandro Moschitti:
Efficient Online Learning for Mapping Kernels on Linguistic Structures. AAAI 2019: 3421-3428 - [c48]Mirko Polato, Fabio Aiolli:
Interpretable Preference Learning: A Game Theoretic Framework for Large Margin On-Line Feature and Rule Learning. AAAI 2019: 4723-4730 - [c47]Guglielmo Faggioli, Mirko Polato, Ivano Lauriola, Fabio Aiolli:
Evaluation of Tag Clusterings for User Profiling in Movie Recommendation. ICANN (Workshop) 2019: 456-468 - [c46]Mirko Polato, Guglielmo Faggioli, Ivano Lauriola, Fabio Aiolli:
Playing the Large Margin Preference Game. ICANN (2) 2019: 792-804 - [c45]Stefano Campese, Ivano Lauriola, Cristina Scarpazza, Giuseppe Sartori, Fabio Aiolli:
Psychiatric Disorders Classification with 3D Convolutional Neural Networks. INNSBDDL 2019: 48-57 - [c44]Ivano Lauriola, Mirko Polato, Guglielmo Faggioli, Fabio Aiolli:
A Preference-Learning Framework for Modeling Relational Data. INNSBDDL 2019: 359-369 - [c43]Fabio Aiolli, Mauro Conti, Ankit Gangwal, Mirko Polato:
Mind your wallet's privacy: identifying Bitcoin wallet apps and user's actions through network traffic analysis. SAC 2019: 1484-1491 - [c42]Guglielmo Faggioli, Mirko Polato, Fabio Aiolli:
Tag-Based User Profiling: A Game Theoretic Approach. UMAP (Adjunct Publication) 2019: 267-271 - 2018
- [j19]Guido Zampieri, Dinh Tran-Van, Michele Donini, Nicolò Navarin, Fabio Aiolli, Alessandro Sperduti, Giorgio Valle:
Scuba: scalable kernel-based gene prioritization. BMC Bioinform. 19(1): 23:1-23:12 (2018) - [j18]Mirko Polato, Ivano Lauriola, Fabio Aiolli:
A Novel Boolean Kernels Family for Categorical Data. Entropy 20(6): 444 (2018) - [j17]Mirko Polato, Fabio Aiolli:
Boolean kernels for collaborative filtering in top-N item recommendation. Neurocomputing 286: 214-225 (2018) - [j16]Fabio Aiolli, Michael Biehl, Luca Oneto:
Advances in artificial neural networks, machine learning and computational intelligence. Neurocomputing 298: 1-3 (2018) - [j15]Luca Oneto, Nicolò Navarin, Michele Donini, Sandro Ridella, Alessandro Sperduti, Fabio Aiolli, Davide Anguita:
Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels. IEEE Trans. Neural Networks Learn. Syst. 29(10): 4660-4671 (2018) - [c41]Ivano Lauriola, Riccardo Sella, Fabio Aiolli, Alberto Lavelli, Fabio Rinaldi:
Learning Representations for Biomedical NER. NL4AI@AI*IA 2018: 83-94 - [c40]Ivano Lauriola, Mirko Polato, Fabio Aiolli:
The minimum effort maximum output principle applied to Multiple Kernel Learning. ESANN 2018 - [c39]Mirko Polato, Fabio Aiolli:
Boolean kernels for interpretable kernel machines. ESANN 2018 - [c38]Ivano Lauriola, Mirko Polato, Alberto Lavelli, Fabio Rinaldi, Fabio Aiolli:
Learning Preferences for Large Scale Multi-label Problems. ICANN (1) 2018: 546-555 - [c37]Mirko Polato, Fabio Aiolli:
A Game-Theoretic Framework for Interpretable Preference and Feature Learning. ICANN (1) 2018: 659-668 - [c36]Guglielmo Faggioli, Mirko Polato, Fabio Aiolli:
Efficient Similarity Based Methods For The Playlist Continuation Task. RecSys Challenge 2018: 15:1-15:6 - [i4]Mirko Polato, Fabio Aiolli:
Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning. CoRR abs/1812.07895 (2018) - 2017
- [j14]Fabio Aiolli, Gaëlle Bonnet-Loosli, Romain Hérault:
Advances in artificial neural networks, machine learning and computational intelligence. Neurocomputing 268: 1-3 (2017) - [j13]Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita:
Measuring the expressivity of graph kernels through Statistical Learning Theory. Neurocomputing 268: 4-16 (2017) - [j12]Mirko Polato, Fabio Aiolli:
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation. Neurocomputing 268: 17-26 (2017) - [j11]Michele Donini, Fabio Aiolli:
Learning deep kernels in the space of dot product polynomials. Mach. Learn. 106(9-10): 1245-1269 (2017) - [c35]Michele Donini, Nicolò Navarin, Ivano Lauriola, Fabio Aiolli, Fabrizio Costa:
Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning. ESANN 2017 - [c34]Ivano Lauriola, Michele Donini, Fabio Aiolli:
Learning dot-product polynomials for multiclass problems. ESANN 2017 - [c33]Ivano Lauriola, Mirko Polato, Fabio Aiolli:
Radius-Margin Ratio Optimization for Dot-Product Boolean Kernel Learning. ICANN (2) 2017: 183-191 - [c32]Mirko Polato, Ivano Lauriola, Fabio Aiolli:
Classification of Categorical Data in the Feature Space of Monotone DNFs. ICANN (2) 2017: 279-286 - [c31]Mirko Polato, Fabio Aiolli:
Disjunctive Boolean Kernels-based Collaborative Filtering for top-N Item Recommendation. IIR 2017: 97-100 - 2016
- [j10]Fabio Aiolli, Kerstin Bunte, Romain Hérault, Mikhail F. Kanevski:
Special issue: Advances in artificial neural networks, machine learning and computational intelligenceSelected papers from the 23rd European Symposium on Artificial Neural Networks (ESANN 2015). Neurocomputing 192: 1-2 (2016) - [j9]Matteo Ciman, Michele Donini, Ombretta Gaggi, Fabio Aiolli:
Stairstep recognition and counting in a serious Game for increasing users' physical activity. Pers. Ubiquitous Comput. 20(6): 1015-1033 (2016) - [c30]Fabio Aiolli, Mirko Polato:
Kernel based collaborative filtering for very large scale top-N item recommendation. ESANN 2016 - [c29]Luca Oneto, Nicolò Navarin, Michele Donini, Fabio Aiolli, Davide Anguita:
Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints. ESANN 2016 - [c28]Luca Oneto, Nicolò Navarin, Michele Donini, Alessandro Sperduti, Fabio Aiolli, Davide Anguita:
Measuring the Expressivity of Graph Kernels through the Rademacher Complexity. ESANN 2016 - [c27]Mirko Polato, Fabio Aiolli:
A preliminary study on a recommender system for the job recommendation challenge. RecSys Challenge 2016: 1:1-1:4 - [i3]Mirko Polato, Fabio Aiolli:
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation. CoRR abs/1612.05729 (2016) - [i2]Mirko Polato, Fabio Aiolli:
Disjunctive Boolean Kernels for Collaborative Filtering in Top-N Recommendation. CoRR abs/1612.07025 (2016) - 2015
- [j8]Fabio Aiolli, Michele Donini:
EasyMKL: a scalable multiple kernel learning algorithm. Neurocomputing 169: 215-224 (2015) - [j7]Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti:
An Efficient Topological Distance-Based Tree Kernel. IEEE Trans. Neural Networks Learn. Syst. 26(5): 1115-1120 (2015) - [c26]Verónica Bolón-Canedo, Michele Donini, Fabio Aiolli:
Feature and kernel learning. ESANN 2015 - [c25]Fabio Aiolli, Michele Donini, Nicolò Navarin, Alessandro Sperduti:
Multiple Graph-Kernel Learning. SSCI 2015: 1607-1614 - 2014
- [c24]Fabio Aiolli, Michele Donini:
Easy multiple kernel learning. ESANN 2014 - [c23]Fabio Aiolli, Michele Donini:
Learning Anisotropic RBF Kernels. ICANN 2014: 515-522 - [c22]Fabio Aiolli, Matteo Ciman, Michele Donini, Ombretta Gaggi:
ClimbTheWorld: real-time stairstep counting to increase physical activity. MobiQuitous 2014: 218-227 - [c21]Fabio Aiolli:
Convex AUC optimization for top-N recommendation with implicit feedback. RecSys 2014: 293-296 - 2013
- [c20]Fabio Aiolli:
A Preliminary Study on a Recommender System for the Million Songs Dataset Challenge. IIR 2013: 73-83 - [c19]Fabio Aiolli:
Efficient top-n recommendation for very large scale binary rated datasets. RecSys 2013: 273-280 - 2012
- [j6]Tiziana Sanavia, Fabio Aiolli, Giovanni Da San Martino, Andrea Bisognin, Barbara Di Camillo:
Improving biomarker list stability by integration of biological knowledge in the learning process. BMC Bioinform. 13(S-4): S22 (2012) - [c18]Fabio Aiolli:
Transfer Learning by Kernel Meta-Learning. ICML Unsupervised and Transfer Learning 2012: 81-95 - 2011
- [c17]Fabio Aiolli, Andrea Burattin, Alessandro Sperduti:
A Business Process Metric Based on the Alpha Algorithm Relations. Business Process Management Workshops (1) 2011: 141-146 - [c16]Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti:
Extending Tree Kernels with Topological Information. ICANN (1) 2011: 142-149 - 2010
- [c15]Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti:
A New Tree Kernel Based on SOM-SD. ICANN (2) 2010: 49-58 - [p3]Fabio Aiolli, Alessandro Sperduti:
A Preference Optimization Based Unifying Framework for Supervised Learning Problems. Preference Learning 2010: 19-42
2000 – 2009
- 2009
- [j5]Fabio Aiolli, Claudio E. Palazzi:
Enhancing Artificial Intelligence on a Real Mobile Game. Int. J. Comput. Games Technol. 2009: 456169:1-456169:9 (2009) - [j4]Fabio Aiolli, Riccardo Cardin, Fabrizio Sebastiani, Alessandro Sperduti:
Preferential text classification: learning algorithms and evaluation measures. Inf. Retr. 12(5): 559-580 (2009) - [j3]Fabio Aiolli, Giovanni Da San Martino, Markus Hagenbuchner, Alessandro Sperduti:
Learning Nonsparse Kernels by Self-Organizing Maps for Structured Data. IEEE Trans. Neural Networks 20(12): 1938-1949 (2009) - [c14]Fabio Aiolli, Michele De Filippo De Grazia, Alessandro Sperduti:
Application of the preference learning model to a human resources selection task. CIDM 2009: 203-210 - [c13]Fabio Aiolli, Alessandro Sperduti:
Supervised learning as preference optimization. ESANN 2009 - [c12]Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti:
Route kernels for trees. ICML 2009: 17-24 - [p2]Fabio Aiolli, Arianna Ciula:
A case study on the System for Paleographic Inspections (SPI): challenges and new developments. Computational Intelligence and Bioengineering 2009: 53-66 - [p1]Fabio Aiolli, Giovanni Da San Martino, Markus Hagenbuchner, Alessandro Sperduti:
Self-Organizing Maps for Structured Domains: Theory, Models, and Learning of Kernels. Innovations in Neural Information Paradigms and Applications 2009: 9-42 - [i1]Fabio Aiolli, Riccardo Cardin, Fabrizio Sebastiani, Alessandro Sperduti:
Preferential Text Classification: Learning Algorithms and Evaluation Measures. ERCIM News 2009(76) (2009) - 2008
- [c11]Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti:
A Kernel Method for the Optimization of the Margin Distribution. ICANN (1) 2008: 305-314 - [c10]Fabio Aiolli, Claudio Enrico Palazzi:
Enhancing Artificial Intelligence in Games by Learning the Opponent's Playing Style. ECS 2008: 1-10 - 2007
- [c9]Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti, Alessandro Moschitti:
Efficient Kernel-based Learning for Trees. CIDM 2007: 308-315 - [c8]Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti, Markus Hagenbuchner:
"Kernelized" Self-Organizing Maps for Structured Data. ESANN 2007: 19-24 - [c7]Fabio Aiolli, Fabrizio Sebastiani, Alessandro Sperduti:
Preference Learning for Category-Ranking based Interactive Text Categorization. IJCNN 2007: 2034-2039 - 2006
- [c6]Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti, Alessandro Moschitti:
Fast On-line Kernel Learning for Trees. ICDM 2006: 787-791 - 2005
- [j2]Fabio Aiolli, Alessandro Sperduti:
Multiclass Classification with Multi-Prototype Support Vector Machines. J. Mach. Learn. Res. 6: 817-850 (2005) - [c5]Fabio Aiolli:
A Preference Model for Structured Supervised Learning Tasks. ICDM 2005: 557-560 - 2004
- [b1]Fabio Aiolli:
Large margin multiclass learning. University of Pisa, Italy, 2004 - [c4]Fabio Aiolli, Alessandro Sperduti:
Learning Preferences for Multiclass Problems. NIPS 2004: 17-24 - 2003
- [c3]Fabio Aiolli, Alessandro Sperduti:
Multi-prototype Support Vector Machine. IJCAI 2003: 541-546 - 2002
- [j1]Fabio Aiolli, Alessandro Sperduti:
A re-weighting strategy for improving margins. Artif. Intell. 137(1-2): 197-216 (2002) - [c2]Fabio Aiolli, Alessandro Sperduti:
An efficient SMO-like algorithm for multiclass SVM. NNSP 2002: 297-306 - 2001
- [c1]Fabio Aiolli, Alessandro Sperduti:
A Simple Additive Re-weighting Strategy for Improving Margins. IJCAI 2001: 927-934
Coauthor Index
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