Conference Papers

  • Antonino Freno (2017): “Practical Lessons from Developing a Large-Scale Recommender System at Zalando”, Proceedings of the 11th ACM Conference on Recommender Systems (RecSys 2017). ACM, pp. 251–259.
  • Antonino Freno, Martin Saveski, Rodolphe Jenatton, and Cédric Archambeau (2015): “OnePass Ranking Models for Low-Latency Product Recommendations”, Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2015). ACM, pp. 1789–1798.
  • Antonino Freno, Mikaela Keller, and Marc Tommasi (2012): “Fiedler Random Fields: A Large-Scale Spectral Approach to Statistical Network Modeling”. Advances in Neural Information Processing Systems 25 (NIPS 2012), MIT Press.
  • Antonino Freno, Mikaela Keller, Gemma C. Garriga, and Marc Tommasi (2012): “Spectral Estimation of Conditional Random Graph Models for Large-Scale Network Data”. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI 2012), AUAI Press, pp. 265–274.
  • Antonino Freno (2012): “Semiparametric Pseudo-Likelihood Estimation in Markov Random Fields”. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2012), JMLR W&CP 22, pp. 391–399.
  • Antonino Freno, Tiziano Papini, and Michelangelo Diligenti (2011): “Learning to Rank using Markov Random Fields”. Proceedings of the 10th International Conference on Machine Learning and Applications (ICMLA 2011).
  • Antonino Freno, Edmondo Trentin, and Marco Gori (2010): “Kernel-Based Hybrid Random Fields for Nonparametric Density Estimation”. Proceedings of the 19th European Conference on Artificial Intelligence (ECAI 2010). IOS Press, pp. 427–432.
  • Antonino Freno, Edmondo Trentin, and Marco Gori (2009): “Scalable Pseudo-Likelihood Estimation in Hybrid Random Fields”, Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2009). ACM, pp. 319–327.
  • Antonino Freno, Edmondo Trentin, and Marco Gori (2009): “Scalable Statistical Learning: A Modular Bayesian/Markov Network Approach”, Proceedings of the International Joint Conference on Neural Networks (IJCNN 2009). IEEE, pp. 890–897.
  • Edmondo Trentin and Antonino Freno (2009): “Unsupervised Nonparametric Density Estimation: A Neural Network Approach”, Proceedings of the International Joint Conference on Neural Networks (IJCNN 2009). IEEE, pp. 3140–3147.
  • Antonino Freno (2007): “Selecting Features by Learning Markov Blankets”. In B. Apolloni, R. J. Howlett, and L. C. Jain (eds.): Knowledge-Based Intelligent Information and Engineering Systems: KES 2007 – WIRN 2007, Part I (LNAI 4692). Berlin: Springer-Verlag, pp. 69–76.

Journal Papers

  • Marco Bongini, Antonino Freno, Vincenzo Laveglia, and Edmondo Trentin (2018): “Dynamic Hybrid Random Fields for the Probabilistic Graphical Modeling of Sequential Data: Definitions, Algorithms, and an Application to Bioinformatics”. Neural Processing Letters, 48: pp. 733–768.
  • Marco Aste, Massimo Boninsegna, Antonino Freno, and Edmondo Trentin (2015): “Techniques for Dealing with Incomplete Data: A Tutorial and Survey”. Pattern Analysis and Applications, 18, pp. 1–29.
  • Antonino Freno, Edmondo Trentin, and Marco Gori (2009): “A Hybrid Random Field Model for Scalable Statistical Learning”. Neural Networks, 22, pp. 603–613.
  • Antonino Freno (2009): “Statistical Machine Learning and the Logic of Scientific Discovery”. Iris. European Journal of Philosophy and Public Debate, 1, pp. 375–388.

Books

  • Antonino Freno, Edmondo Trentin (2011): Hybrid Random Fields. A Scalable Approach to Parameter and Structure Learning in Probabilistic Graphical Models. Springer-Verlag, 2011.

Book Chapters

  • Antonino Freno (2018): “Clothing Recommendations: The Zalando Case”. In Shlomo Berkovsky, Iván Cantador, Domonkos Tikk (eds.): Collaborative Recommendations. Algorithms, Practical Challenges and Applications: WorldScientific, pp. 687–711.
  • Antonino Freno (2010): “Probabilistic Graphical Models and the Logic of Scientific Discovery”. In M. D’Agostino, G. Giorello, F. Laudisa, T. Pievani, C. Sinigaglia (eds.): New Essays in Logic and Philosophy of Science. SILFS 1. London (UK): College Publications, pp. 617–628.
  • Edmondo Trentin and Antonino Freno (2009): “Probabilistic Interpretation of Neural Networks for the Classification of Vectors, Sequences, and Graphs”. In M. Bianchini, L. C. Jain, M. Maggini, F. Scarselli (eds.): Innovations in Neural Information Paradigms and Applications. Springer-Verlag, pp. 155–182.

Workshop Papers

  • Antonino Freno, Gemma C. Garriga, and Mikaela Keller (2011): “Learning to Recommend Links using Graph Structure and Node Content”. NIPS 2011 Workshop on Choice Models and Preference Learning.