A Simple Additive Re-weighting Strategy for Improving Margins. - Fabio Aiolli; Alessandro Sperduti (IJCAI, 2001)

An efficient SMO-like algorithm for multiclass SVM. - Fabio Aiolli; Alessandro Sperduti (NNSP, 2002)

A re-weighting strategy for improving margins. - Fabio Aiolli; Alessandro Sperduti (Artif. Intell., 2002)

Multi-prototype Support Vector Machine. - Fabio Aiolli; Alessandro Sperduti (IJCAI, 2003)

Learning Preferences for Multiclass Problems. - Fabio Aiolli; Alessandro Sperduti (NIPS, 2004)

Large margin multiclass learning. - Fabio Aiolli (2004)

A Preference Model for Structured Supervised Learning Tasks. - Fabio Aiolli (ICDM, 2005)

Multiclass Classification with Multi-Prototype Support Vector Machines. - Fabio Aiolli; Alessandro Sperduti (J. Mach. Learn. Res., 2005)

Fast On-line Kernel Learning for Trees. - Fabio Aiolli; Giovanni Da San Martino; Alessandro Sperduti; Alessandro Moschitti (ICDM, 2006)

Efficient Kernel-based Learning for Trees. - Fabio Aiolli; Giovanni Da San Martino; Alessandro Sperduti; Alessandro Moschitti (CIDM, 2007)

"Kernelized" Self-Organizing Maps for Structured Data. - Fabio Aiolli; Giovanni Da San Martino; Alessandro Sperduti; Markus Hagenbuchner (ESANN, 2007)

Preference Learning for Category-Ranking based Interactive Text Categorization. - Fabio Aiolli; Fabrizio Sebastiani 001; Alessandro Sperduti (IJCNN, 2007)

A Kernel Method for the Optimization of the Margin Distribution. - Fabio Aiolli; Giovanni Da San Martino; Alessandro Sperduti (ICANN (1), 2008)

Enhancing Artificial Intelligence in Games by Learning the Opponent's Playing Style. - Fabio Aiolli; Claudio Enrico Palazzi (ECS, 2008)

Application of the preference learning model to a human resources selection task. - Fabio Aiolli; Michele De Filippo De Grazia; Alessandro Sperduti (CIDM, 2009)

Supervised learning as preference optimization. - Fabio Aiolli; Alessandro Sperduti (ESANN, 2009)

Route kernels for trees. - Fabio Aiolli; Giovanni Da San Martino; Alessandro Sperduti (ICML, 2009)

Preferential Text Classification: Learning Algorithms and Evaluation Measures. - Fabio Aiolli; Riccardo Cardin; Fabrizio Sebastiani 001; Alessandro Sperduti (ERCIM News, 2009)

Enhancing Artificial Intelligence on a Real Mobile Game. - Fabio Aiolli; Claudio E. Palazzi (Int. J. Comput. Games Technol., 2009)

Learning Nonsparse Kernels by Self-Organizing Maps for Structured Data. - Fabio Aiolli; Giovanni Da San Martino; Markus Hagenbuchner; Alessandro Sperduti (IEEE Trans. Neural Networks, 2009)

A case study on the System for Paleographic Inspections (SPI): challenges and new developments. - Fabio Aiolli; Arianna Ciula (Computational Intelligence and Bioengineering, 2009)

Self-Organizing Maps for Structured Domains: Theory, Models, and Learning of Kernels. - Fabio Aiolli; Giovanni Da San Martino; Markus Hagenbuchner; Alessandro Sperduti (Innovations in Neural Information Paradigms and Applications, 2009)

A Preference Optimization Based Unifying Framework for Supervised Learning Problems. - Fabio Aiolli; Alessandro Sperduti (Preference Learning, 2010)

A New Tree Kernel Based on SOM-SD. - Fabio Aiolli; Giovanni Da San Martino; Alessandro Sperduti (ICANN (2), 2010)

A Business Process Metric Based on the Alpha Algorithm Relations. - Fabio Aiolli; Andrea Burattin; Alessandro Sperduti (Business Process Management Workshops (1), 2011)

Extending Tree Kernels with Topological Information. - Fabio Aiolli; Giovanni Da San Martino; Alessandro Sperduti (ICANN (1), 2011)

Improving biomarker list stability by integration of biological knowledge in the learning process. - Tiziana Sanavia; Fabio Aiolli; Giovanni Da San Martino; Andrea Bisognin; Barbara Di Camillo (BMC Bioinform., 2012)

Transfer Learning by Kernel Meta-Learning. - Fabio Aiolli (ICML Unsupervised and Transfer Learning, 2012)

A Preliminary Study on a Recommender System for the Million Songs Dataset Challenge. - Fabio Aiolli (IIR, 2013)

Efficient top-n recommendation for very large scale binary rated datasets. - Fabio Aiolli (RecSys, 2013)

Easy multiple kernel learning. - Fabio Aiolli; Michele Donini (ESANN, 2014)

Learning Anisotropic RBF Kernels. - Fabio Aiolli; Michele Donini (ICANN, 2014)

ClimbTheWorld: real-time stairstep counting to increase physical activity. - Fabio Aiolli; Matteo Ciman; Michele Donini; Ombretta Gaggi (MobiQuitous, 2014)

Convex AUC optimization for top-N recommendation with implicit feedback. - Fabio Aiolli (RecSys, 2014)

Feature and kernel learning. - Verónica Bolón-Canedo; Michele Donini; Fabio Aiolli (ESANN, 2015)

Multiple Graph-Kernel Learning. - Fabio Aiolli; Michele Donini; Nicolò Navarin; Alessandro Sperduti (SSCI, 2015)

EasyMKL: a scalable multiple kernel learning algorithm. - Fabio Aiolli; Michele Donini (Neurocomputing, 2015)

An Efficient Topological Distance-Based Tree Kernel. - Fabio Aiolli; Giovanni Da San Martino; Alessandro Sperduti (IEEE Trans. Neural Networks Learn. Syst., 2015)

Kernel based collaborative filtering for very large scale top-N item recommendation. - Fabio Aiolli; Mirko Polato (ESANN, 2016)

Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints. - Luca Oneto; Nicolò Navarin; Michele Donini; Fabio Aiolli; Davide Anguita (ESANN, 2016)

Measuring the Expressivity of Graph Kernels through the Rademacher Complexity. - Luca Oneto; Nicolò Navarin; Michele Donini; Alessandro Sperduti; Fabio Aiolli; Davide Anguita (ESANN, 2016)

A preliminary study on a recommender system for the job recommendation challenge. - Mirko Polato; Fabio Aiolli (RecSys Challenge, 2016)

Disjunctive Boolean Kernels for Collaborative Filtering in Top-N Recommendation. - Mirko Polato; Fabio Aiolli (CoRR, 2016)

Special issue: Advances in artificial neural networks, machine learning and computational intelligenceSelected papers from the 23rd European Symposium on Artificial Neural Networks (ESANN 2015). - Fabio Aiolli; Kerstin Bunte; Romain Hérault; Mikhail F. Kanevski (Neurocomputing, 2016)

Stairstep recognition and counting in a serious Game for increasing users' physical activity. - Matteo Ciman; Michele Donini; Ombretta Gaggi; Fabio Aiolli (Pers. Ubiquitous Comput., 2016)

Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning. - Michele Donini; Nicolò Navarin; Ivano Lauriola; Fabio Aiolli; Fabrizio Costa (ESANN, 2017)

Learning dot-product polynomials for multiclass problems. - Ivano Lauriola; Michele Donini; Fabio Aiolli (ESANN, 2017)

Radius-Margin Ratio Optimization for Dot-Product Boolean Kernel Learning. - Ivano Lauriola; Mirko Polato; Fabio Aiolli (ICANN (2), 2017)

Classification of Categorical Data in the Feature Space of Monotone DNFs. - Mirko Polato; Ivano Lauriola; Fabio Aiolli (ICANN (2), 2017)

Disjunctive Boolean Kernels-based Collaborative Filtering for top-N Item Recommendation. - Mirko Polato; Fabio Aiolli (IIR, 2017)

Advances in artificial neural networks, machine learning and computational intelligence. - Fabio Aiolli; Gaëlle Bonnet-Loosli; Romain Hérault (Neurocomputing, 2017)

Measuring the expressivity of graph kernels through Statistical Learning Theory. - Luca Oneto; Nicolò Navarin; Michele Donini; Alessandro Sperduti; Fabio Aiolli; Davide Anguita (Neurocomputing, 2017)

Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation. - Mirko Polato; Fabio Aiolli (Neurocomputing, 2017)

Learning deep kernels in the space of dot product polynomials. - Michele Donini; Fabio Aiolli (Mach. Learn., 2017)

Learning Representations for Biomedical NER. - Ivano Lauriola; Riccardo Sella; Fabio Aiolli; Alberto Lavelli; Fabio Rinaldi 001 (NL4AI@AI*IA, 2018)

The minimum effort maximum output principle applied to Multiple Kernel Learning. - Ivano Lauriola; Mirko Polato; Fabio Aiolli (ESANN, 2018)

Boolean kernels for interpretable kernel machines. - Mirko Polato; Fabio Aiolli (ESANN, 2018)

Learning Preferences for Large Scale Multi-label Problems. - Ivano Lauriola; Mirko Polato; Alberto Lavelli; Fabio Rinaldi 001; Fabio Aiolli (ICANN (1), 2018)

A Game-Theoretic Framework for Interpretable Preference and Feature Learning. - Mirko Polato; Fabio Aiolli (ICANN (1), 2018)

Efficient Similarity Based Methods For The Playlist Continuation Task. - Guglielmo Faggioli; Mirko Polato; Fabio Aiolli (RecSys Challenge, 2018)

Scuba: scalable kernel-based gene prioritization. - Guido Zampieri; Dinh Tran-Van; Michele Donini; Nicolò Navarin; Fabio Aiolli; Alessandro Sperduti; Giorgio Valle (BMC Bioinform., 2018)

A Novel Boolean Kernels Family for Categorical Data. - Mirko Polato; Ivano Lauriola; Fabio Aiolli (Entropy, 2018)

Advances in artificial neural networks, machine learning and computational intelligence. - Fabio Aiolli; Michael Biehl; Luca Oneto (Neurocomputing, 2018)

Boolean kernels for collaborative filtering in top-N item recommendation. - Mirko Polato; Fabio Aiolli (Neurocomputing, 2018)

Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels. - Luca Oneto; Nicolò Navarin; Michele Donini; Sandro Ridella; Alessandro Sperduti; Fabio Aiolli; Davide Anguita (IEEE Trans. Neural Networks Learn. Syst., 2018)

Efficient Online Learning for Mapping Kernels on Linguistic Structures. - Giovanni Da San Martino; Alessandro Sperduti; Fabio Aiolli; Alessandro Moschitti (AAAI, 2019)

Interpretable Preference Learning: A Game Theoretic Framework for Large Margin On-Line Feature and Rule Learning. - Mirko Polato; Fabio Aiolli (AAAI, 2019)

Evaluation of Tag Clusterings for User Profiling in Movie Recommendation. - Guglielmo Faggioli; Mirko Polato; Ivano Lauriola; Fabio Aiolli (ICANN (Workshop), 2019)

Playing the Large Margin Preference Game. - Mirko Polato; Guglielmo Faggioli; Ivano Lauriola; Fabio Aiolli (ICANN (2), 2019)

Psychiatric Disorders Classification with 3D Convolutional Neural Networks. - Stefano Campese; Ivano Lauriola; Cristina Scarpazza; Giuseppe Sartori; Fabio Aiolli (INNSBDDL, 2019)

A Preference-Learning Framework for Modeling Relational Data. - Ivano Lauriola; Mirko Polato; Guglielmo Faggioli; Fabio Aiolli (INNSBDDL, 2019)

Mind your wallet's privacy: identifying Bitcoin wallet apps and user's actions through network traffic analysis. - Fabio Aiolli; Mauro Conti; Ankit Gangwal; Mirko Polato (SAC, 2019)

Tag-Based User Profiling: A Game Theoretic Approach. - Guglielmo Faggioli; Mirko Polato; Fabio Aiolli (UMAP (Adjunct Publication), 2019)

Boolean kernels for rule based interpretation of support vector machines. - Mirko Polato; Fabio Aiolli (Neurocomputing, 2019)

Automatic Detection of Cross-language Verbal Deception. - Pasquale Capuozzo; Ivano Lauriola; Carlo Strapparava; Fabio Aiolli; Giuseppe Sartori (CogSci, 2020)

Exploring the feature space of character-level embeddings. - Ivano Lauriola; Stefano Campese; Alberto Lavelli; Fabio Rinaldi 001; Fabio Aiolli (ESANN, 2020)

Language processing in the era of deep learning. - Ivano Lauriola; Alberto Lavelli; Fabio Aiolli (ESANN, 2020)

Big Enough to Care Not Enough to Scare! Crawling to Attack Recommender Systems. - Fabio Aiolli; Mauro Conti; Stjepan Picek; Mirko Polato (ESORICS (2), 2020)

Monotone Deep Spectrum Kernels. - Ivano Lauriola; Fabio Aiolli (ICANN (1), 2020)

DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text. - Pasquale Capuozzo; Ivano Lauriola; Carlo Strapparava; Fabio Aiolli; Giuseppe Sartori (LREC, 2020)

A Look Inside the Black-Box: Towards the Interpretability of Conditioned Variational Autoencoder for Collaborative Filtering. - Tommaso Carraro; Mirko Polato; Fabio Aiolli (UMAP (Adjunct Publication), 2020)

Recency Aware Collaborative Filtering for Next Basket Recommendation. - Guglielmo Faggioli; Mirko Polato; Fabio Aiolli (UMAP, 2020)

Conditioned Variational Autoencoder for top-N item recommendation. - Mirko Polato; Tommaso Carraro; Fabio Aiolli (CoRR, 2020)

MKLpy: a python-based framework for Multiple Kernel Learning. - Ivano Lauriola; Fabio Aiolli (CoRR, 2020)

Enhancing deep neural networks via multiple kernel learning. - Ivano Lauriola; Claudio Gallicchio; Fabio Aiolli (Pattern Recognit., 2020)

Learning deep kernels in the space of monotone conjunctive polynomials. - Ivano Lauriola; Mirko Polato; Fabio Aiolli (Pattern Recognit. Lett., 2020)

Privacy-Preserving Kernel Computation For Vertically Partitioned Data. - Mirko Polato; Alberto Gallinaro; Fabio Aiolli (ESANN, 2021)

Exploring the structure of BERT through Kernel Learning. - Ivano Lauriola; Alberto Lavelli; Alessandro Moschitti; Fabio Aiolli (IJCNN, 2021)

Learning adaptive representations for entity recognition in the biomedical domain. - Ivano Lauriola; Fabio Aiolli; Alberto Lavelli; Fabio Rinaldi 001 (J. Biomed. Semant., 2021)

Propositional Kernels. - Mirko Polato; Fabio Aiolli (Entropy, 2021)

Logic Tensor Networks for Top-N Recommendation. - Tommaso Carraro; Alessandro Daniele; Fabio Aiolli; Luciano Serafini (AI*IA, 2022)

Price direction prediction in financial markets, using Random Forest and Adaboost. - Mohammadmahdi Ghahramani; Fabio Aiolli (ESANN, 2022)

Bayes Point Rule Set Learning. - Mirko Polato; Fabio Aiolli; Luca Bergamin; Tommaso Carraro (ESANN, 2022)

Conditioned Variational Autoencoder for Top-N Item Recommendation. - Tommaso Carraro; Mirko Polato; Luca Bergamin; Fabio Aiolli (ICANN (2), 2022)

Novel Applications for VAE-based Anomaly Detection Systems. - Luca Bergamin; Tommaso Carraro; Mirko Polato; Fabio Aiolli (IJCNN, 2022)

An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools. - Ivano Lauriola; Alberto Lavelli; Fabio Aiolli (Neurocomputing, 2022)

PRL: A game theoretic large margin method for interpretable feature learning. - Mirko Polato; Guglielmo Faggioli; Fabio Aiolli (Neurocomputing, 2022)

On the feasibility of crawling-based attacks against recommender systems. - Fabio Aiolli; Mauro Conti; Stjepan Picek; Mirko Polato (J. Comput. Secur., 2022)

Graph-based Explainable Recommendation Systems: Are We Rigorously Evaluating Explanations? - Andrea Montagna; Alvise De Biasio; Nicolò Navarin; Fabio Aiolli (HCAI4U@CHItaly, 2023)

Mitigating Data Sparsity via Neuro-Symbolic Knowledge Transfer. - Tommaso Carraro; Alessandro Daniele; Fabio Aiolli; Luciano Serafini (KaRS@RecSys, 2023)

A systematic review of value-aware recommender systems. - Alvise De Biasio; Andrea Montagna; Fabio Aiolli; Nicolò Navarin (Expert Syst. Appl., 2023)

Addressing Challenges in Image Translation for Contrast-Enhanced Mammography using Generative Adversarial Networks. - Mohammad Hosseinipour; Luca Bergamin; Harel Kotler; Gisella Gennaro; Fabio Aiolli (HC@AIxIA, 2024)

Integrating L0 regularization into Multi-layer Logical Perceptron for Interpretable Classification. - Gonzalo Jaimovitch-López; Luca Bergamin; Fabio Aiolli; Roberto Confalonieri 001 (OVERLAY, 2024)

LTNtorch: PyTorch Implementation of Logic Tensor Networks. - Tommaso Carraro; Luciano Serafini; Fabio Aiolli (CoRR, 2024)

An investigation into creating counterfactual examples for non-linear Support Vector Machines. - Luca Bergamin; Fabio Aiolli (Neurocomputing, 2025)

Improving rule-based classifiers by Bayes point aggregation. - Luca Bergamin; Mirko Polato; Fabio Aiolli (Neurocomputing, 2025)