Fabio Aiolli

Proceedings of the First Workshop on User Perspectives in Human-Centred Artificial Intelligence (HCAI4U 2023) co-located with the 15th Biannual Conference of the Italian SIGCHI Chapter (CHItaly 2023), 2023

2022

On the feasibility of crawling-based attacks against recommender systems.

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[DOI]

J. Comput. Secur., 2022

PRL: A game theoretic large margin method for interpretable feature learning.

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[DOI]

Neurocomputing, 2022

An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools.

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[DOI]

Alberto Lavelli

Neurocomputing, 2022

Logic Tensor Networks for Top-N Recommendation.

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[DOI]

Proceedings of the 16th International Workshop on Neural-Symbolic Learning and Reasoning as part of the 2nd International Joint Conference on Learning & Reasoning (IJCLR 2022), 2022

Novel Applications for VAE-based Anomaly Detection Systems.

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[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2022

Conditioned Variational Autoencoder for Top-N Item Recommendation.

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[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2022, 2022

Bayes Point Rule Set Learning.

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[DOI]

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

Price direction prediction in financial markets, using Random Forest and Adaboost.

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[DOI]

Mohammadmahdi Ghahramani

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

2021

Propositional Kernels.

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[DOI]

Entropy, 2021

Learning adaptive representations for entity recognition in the biomedical domain.

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[DOI]

J. Biomed. Semant., 2021

Exploring the structure of BERT through Kernel Learning.

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[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2021

Privacy-Preserving Kernel Computation For Vertically Partitioned Data.

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[DOI]

Alberto Gallinaro

Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

2020

Learning deep kernels in the space of monotone conjunctive polynomials.

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[DOI]

Pattern Recognit. Lett., 2020

Enhancing deep neural networks via multiple kernel learning.

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[DOI]

Claudio Gallicchio

Pattern Recognit., 2020

MKLpy: a python-based framework for Multiple Kernel Learning.

[BibT_eX]

[DOI]

CoRR, 2020

Conditioned Variational Autoencoder for top-N item recommendation.

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[DOI]

Tommaso Carraro

CoRR, 2020

Recency Aware Collaborative Filtering for Next Basket Recommendation.

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[DOI]

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 2020

A Look Inside the Black-Box: Towards the Interpretability of Conditioned Variational Autoencoder for Collaborative Filtering.

[BibT_eX]

[DOI]

Tommaso Carraro

Proceedings of the Adjunct Publication of the 28th ACM Conference on User Modeling, 2020

DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text.

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[DOI]

Proceedings of The 12th Language Resources and Evaluation Conference, 2020

Monotone Deep Spectrum Kernels.

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[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2020, 2020

Big Enough to Care Not Enough to Scare! Crawling to Attack Recommender Systems.

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[DOI]

Proceedings of the Computer Security - ESORICS 2020, 2020

Language processing in the era of deep learning.

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[DOI]

Alberto Lavelli

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

Exploring the feature space of character-level embeddings.

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[DOI]

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

Automatic Detection of Cross-language Verbal Deception.

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[DOI]

Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

2019

Boolean kernels for rule based interpretation of support vector machines.

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[DOI]

Neurocomputing, 2019

Tag-Based User Profiling: A Game Theoretic Approach.

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[DOI]

Proceedings of the Adjunct Publication of the 27th Conference on User Modeling, 2019

Mind your wallet's privacy: identifying Bitcoin wallet apps and user's actions through network traffic analysis.

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[DOI]

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, 2019

A Preference-Learning Framework for Modeling Relational Data.

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[DOI]

Proceedings of the Recent Advances in Big Data and Deep Learning, 2019

Psychiatric Disorders Classification with 3D Convolutional Neural Networks.

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[DOI]

Proceedings of the Recent Advances in Big Data and Deep Learning, 2019

Playing the Large Margin Preference Game.

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[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019: Deep Learning, 2019

Evaluation of Tag Clusterings for User Profiling in Movie Recommendation.

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[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2019 - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, 2019

Interpretable Preference Learning: A Game Theoretic Framework for Large Margin On-Line Feature and Rule Learning.

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[DOI]

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Efficient Online Learning for Mapping Kernels on Linguistic Structures.

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[DOI]

Alessandro Moschitti

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels.

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[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2018

Boolean kernels for collaborative filtering in top-N item recommendation.

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[DOI]

Neurocomputing, 2018

Advances in artificial neural networks, machine learning and computational intelligence.

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[DOI]

Michael Biehl

Luca Oneto

Neurocomputing, 2018

A Novel Boolean Kernels Family for Categorical Data.

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[DOI]

Entropy, 2018

Scuba: scalable kernel-based gene prioritization.

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[DOI]

BMC Bioinform., 2018

Efficient Similarity Based Methods For The Playlist Continuation Task.

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[DOI]

Proceedings of the ACM Recommender Systems Challenge, 2018

A Game-Theoretic Framework for Interpretable Preference and Feature Learning.

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[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018

Learning Preferences for Large Scale Multi-label Problems.

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[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018

Boolean kernels for interpretable kernel machines.

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[DOI]

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

The minimum effort maximum output principle applied to Multiple Kernel Learning.

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[DOI]

Proceedings of the 26th European Symposium on Artificial Neural Networks, 2018

Learning Representations for Biomedical NER.

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[DOI]

Proceedings of the 2nd Workshop on Natural Language for Artificial Intelligence (NL4AI 2018) co-located with 17th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2018), 2018

2017

Learning deep kernels in the space of dot product polynomials.

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[DOI]

Mach. Learn., 2017

Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation.

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[DOI]

Neurocomputing, 2017

Measuring the expressivity of graph kernels through Statistical Learning Theory.

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[DOI]

Neurocomputing, 2017

Advances in artificial neural networks, machine learning and computational intelligence.

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[DOI]

Gaëlle Bonnet-Loosli

Romain Hérault

Neurocomputing, 2017

Disjunctive Boolean Kernels-based Collaborative Filtering for top-N Item Recommendation.

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[DOI]

Proceedings of the 8th Italian Information Retrieval Workshop, 2017

Classification of Categorical Data in the Feature Space of Monotone DNFs.

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[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2017, 2017

Radius-Margin Ratio Optimization for Dot-Product Boolean Kernel Learning.

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[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2017, 2017

Learning dot-product polynomials for multiclass problems.

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Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

Fast hyperparameter selection for graph kernels via subsampling and multiple kernel learning.

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Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

2016

Stairstep recognition and counting in a serious Game for increasing users' physical activity.

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Pers. Ubiquitous Comput., 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).

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[DOI]

Neurocomputing, 2016

Disjunctive Boolean Kernels for Collaborative Filtering in Top-N Recommendation.

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[DOI]

CoRR, 2016

A preliminary study on a recommender system for the job recommendation challenge.

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Proceedings of the 2016 Recommender Systems Challenge, 2016

Measuring the Expressivity of Graph Kernels through the Rademacher Complexity.

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Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

Advances in Learning with Kernels: Theory and Practice in a World of growing Constraints.

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Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

Kernel based collaborative filtering for very large scale top-N item recommendation.

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Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

2015

An Efficient Topological Distance-Based Tree Kernel.

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[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2015

EasyMKL: a scalable multiple kernel learning algorithm.

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[DOI]

Neurocomputing, 2015

Multiple Graph-Kernel Learning.

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[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2015

Feature and kernel learning.

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[DOI]

Verónica Bolón-Canedo

Proceedings of the 23rd European Symposium on Artificial Neural Networks, 2015

2014

Convex AUC optimization for top-N recommendation with implicit feedback.

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[DOI]

Proceedings of the Eighth ACM Conference on Recommender Systems, 2014

ClimbTheWorld: real-time stairstep counting to increase physical activity.

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[DOI]

Proceedings of the 11th International Conference on Mobile and Ubiquitous Systems: Computing, 2014

Learning Anisotropic RBF Kernels.

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[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2014, 2014

Easy multiple kernel learning.

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[DOI]

Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014

2013

Efficient top-n recommendation for very large scale binary rated datasets.

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[DOI]

Proceedings of the Seventh ACM Conference on Recommender Systems, 2013

A Preliminary Study on a Recommender System for the Million Songs Dataset Challenge.

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[DOI]

Proceedings of the 4th Italian Information Retrieval Workshop, 2013

2012

Transfer Learning by Kernel Meta-Learning.

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[DOI]

Proceedings of the Unsupervised and Transfer Learning, 2012

Improving biomarker list stability by integration of biological knowledge in the learning process.

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[DOI]

Tiziana Sanavia

Andrea Bisognin

Barbara Di Camillo

BMC Bioinform., 2012

2011

Extending Tree Kernels with Topological Information.

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[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

A Business Process Metric Based on the Alpha Algorithm Relations.

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[DOI]

Andrea Burattin

Proceedings of the Business Process Management Workshops, 2011

2010

A New Tree Kernel Based on SOM-SD.

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[DOI]

Proceedings of the Artificial Neural Networks, 2010

A Preference Optimization Based Unifying Framework for Supervised Learning Problems.

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[DOI]

Proceedings of the Preference Learning., 2010

2009

Self-Organizing Maps for Structured Domains: Theory, Models, and Learning of Kernels.

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[DOI]

Markus Hagenbuchner

Proceedings of the Innovations in Neural Information Paradigms and Applications, 2009

A case study on the System for Paleographic Inspections (SPI): challenges and new developments.

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[DOI]

Arianna Ciula

Proceedings of the Computational Intelligence and Bioengineering, 2009

Learning Nonsparse Kernels by Self-Organizing Maps for Structured Data.

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[DOI]

Markus Hagenbuchner

IEEE Trans. Neural Networks, 2009

Enhancing Artificial Intelligence on a Real Mobile Game.

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[DOI]

Claudio E. Palazzi

Int. J. Comput. Games Technol., 2009

Preferential Text Classification: Learning Algorithms and Evaluation Measures.

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[DOI]

ERCIM News, 2009

Route kernels for trees.

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[DOI]

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Supervised learning as preference optimization.

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[DOI]

Proceedings of the 17th European Symposium on Artificial Neural Networks, 2009

Application of the preference learning model to a human resources selection task.

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[DOI]

Michele De Filippo De Grazia

Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2009

2008

Enhancing Artificial Intelligence in Games by Learning the Opponent's Playing Style.

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[DOI]

Claudio Enrico Palazzi

Proceedings of the New Frontiers for Entertainment Computing, 2008

A Kernel Method for the Optimization of the Margin Distribution.

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[DOI]

Proceedings of the Artificial Neural Networks, 2008

2007

Preference Learning for Category-Ranking based Interactive Text Categorization.

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[DOI]

Fabrizio Sebastiani

Proceedings of the International Joint Conference on Neural Networks, 2007

"Kernelized" Self-Organizing Maps for Structured Data.

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[DOI]

Markus Hagenbuchner

Proceedings of the 15th European Symposium on Artificial Neural Networks, 2007

Efficient Kernel-based Learning for Trees.

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[DOI]

Alessandro Moschitti

Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2007

2006

Fast On-line Kernel Learning for Trees.

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[DOI]

Alessandro Moschitti

Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), 2006

2005

Multiclass Classification with Multi-Prototype Support Vector Machines.

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[DOI]

J. Mach. Learn. Res., 2005

A Preference Model for Structured Supervised Learning Tasks.

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[DOI]

Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005), 2005

2004

Large margin multiclass learning.

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[DOI]

PhD thesis, 2004

Learning Preferences for Multiclass Problems.

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[DOI]

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

2003

Multi-prototype Support Vector Machine.

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[DOI]

Proceedings of the IJCAI-03, 2003

2002

A re-weighting strategy for improving margins.

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[DOI]

Artif. Intell., 2002

An efficient SMO-like algorithm for multiclass SVM.

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[DOI]

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, 2002

2001

A Simple Additive Re-weighting Strategy for Improving Margins.

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