Publications

  1. Hildebrandt, Mireille. 2020 ‘Code-Driven Law: Freezing the Future and Scaling the Past’. In Is Law Computable?: Critical Perspectives on Law and Artificial Intelligence, edited by Christopher Markou and Simon Deakin, Hart Publishing 2020. https://www.bloomsburyprofessional.com/uk/is-law-computable-9781509937066/
  2. Hildebrandt, Mireille. ‘The Adaptive Nature of Text-Driven Law’. Journal of Cross-Disciplinary Research in Computational Law, online first, October 2020 https://journalcrcl.org/crcl/article/view/2.
  3. Hildebrandt, Mireille. 2020. ‘Smart Technologies’. Internet Policy Review 9 (4). https://policyreview.info/concepts/smart-technologies.
  4. Hildebrandt, Mireille 2021 ‘The Issue of Bias. The Framing Powers of Machine Learning’. In Machine We Trust. Perspectives on Dependable AI, edited by Marcello Pelillo and Teresa Scantamburlo. Rochester, NY: MIT Press. https://mitpress.mit.edu/books/machines-we-trust
  1. Benjamins, R. A choices framework for the responsible use of AI. AI Ethics1, 49–53 (2021). https://doi.org/10.1007/s43681-020-00012-5
  1. Nieves Montes and Carles Sierra, Value-Guided Synthesis of Parametric Normative Systems, AAMAS ’21: 20th International Conference on Autonomous Agents and Multiagent Systems, Virtual Event, United Kingdom, May 3-7, 2021
  2. Athina Georgara, Juan A. Rodriguez-Aguilar and Carles Sierra, Towards a Competence-Based Approach to Allocate Teams to Tasks, AAMAS ’21: 20th International Conference on Autonomous Agents and Multiagent Systems, Virtual Event, United Kingdom, May 3-7, 2021.
  3. Dignum, Virginia. “The Myth of Complete AI-Fairness.” International Conference on Artificial Intelligence in Medicine. Springer, Cham, 2021.
  4. Akata, Z., Balliet, D., De Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H. and Hung, H., 2020. A research agenda for hybrid intelligence: augmenting human intellect with collaborative, adaptive, responsible, and explainable artificial intelligence. Computer53(08), pp.18-28.
  5. Dignum, Virginia. “AI — the people and places that make, use and manage it”, Nature. 593(7860): 499-500. May 2021
  6. Dignum, Virginia, and Frank Dignum. “Agents are dead. Long live agents!.” Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS) 2020.
  7. Vinuesa, Ricardo, Hossein Azizpour, Iolanda Leite, Madeline Balaam, Virginia Dignum, Sami Domisch, Anna Felländer, Simone Daniela Langhans, Max Tegmark, and Francesco Fuso Nerini. “The role of artificial intelligence in achieving the Sustainable Development Goals.” Nature communications 11, no. 1 (2020): 1-10.
  8. Aler Tubella, Andrea, Andreas Theodorou, Frank Dignum, and Virginia Dignum. “Governance by Glass-Box: Implementing Transparent Moral Bounds for AI Behaviour.” In 28th International Joint Conference on Artificial Intelligence (IJCAI-19), Macao, China, August 10-16, 2019. 2019.
  9. Dignum, V. (2019). Responsible artificial intelligence: how to develop and use AI in a responsible way. Springer Nature.
  1. Loris Bozzato, Thomas Eiter and Rafael Kiesel (2021). Reasoning on Multi-Relational Contextual Hierarchies via Answer Set Programming with Algebraic Measures. In 37th International Conference on Logic Programming (ICLP 2021). Preprint: https://arxiv.org/abs/2108.03100
  2. Minh Quang Pham, Josep-Maria Crego, François Yvon. Revisiting Multi Domain Machine Translation. Transactions of the Association for Computational Linguistics, The MIT Press, 2021, 9, pp.17-35.
  3. François Buet, François Yvon. Toward Genre Adapted Closed Captioning. Interspeech 2021, Aug 2021, Brno (virtual), Czech Republic. pp.4403-4407
  1. Bettina Fazzinga, Sergio Flesca, Filippo Furfaro (2021) Reasoning over Argument-Incomplete AAFs in the Presence of Correlations. IJCAI 2021: 189-195
  2. Bettina Fazzinga, Sergio Flesca, Filippo Furfaro (2020) Revisiting the Notion of Extension over Incomplete Abstract Argumentation Frameworks. IJCAI 2020: 1712-1718
  3. Bettina Fazzinga, Sergio Flesca, Filippo Furfaro (2020) Embedding the Trust Degrees of Agents in Abstract Argumentation. ECAI 2020: 737-744.
  4. Bruno Veloso, Luciano Caroprese, Matthias König, Sónia Teixeira, Giuseppe Manco, Holger H. Hoos, João Gama: Hyper-parameter Optimization for Latent Spaces. Machine Learning and Knowledge Discovery in Databases. Research Track – European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part III. Lecture Notes in Computer Science 12977, Springer 2021, ISBN 978-3-030-86522-1 pp. 249-264.
  5. Corrado Monti, Giuseppe Manco, Cigdem Aslay, Francesco Bonchi: Learning Ideological Embeddings from Information Cascades. 30th ACM International Conference on Information and Knowledge Management, CIKM 2021, 1-5 November 2021. Queensland, Australia. To Appear.
  6. Angelica Liguori, Giuseppe Manco, Francesco Sergio Pisani, Ettore Ritacco: Adversarial Regularized Reconstruction for Anomaly Detection and Generation. 21st International Conference on Data Mining, ICDM 2021, December 7-10 2021, Auckland, New Zealand. To Appear.
  7. Kashyap Todi, Gilles Bailly, Luis Leiva and Antti Oulasvirta, Adapting User Interfaces with Model-based Reinforcement Learning, CHI ’21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, May 2021 Article No.: 573Pages 1-13 https://doi.org/10.1145/3411764.3445497.
  8. Bulathwela, S., Perez-Ortiz, M., Yilmaz, E., & Shawe-Taylor, J. (2020). TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources. Proceedings of the AAAI Conference on Artificial Intelligence, 34(01), 565-573. https://doi.org/10.1609/aaai.v34i01.5395
  9. Bulathwela, S., Pérez-Ortiz, M., Yilmaz, E., & Shawe-Taylor, J. (2020). Towards an Integrative Educational Recommender for Lifelong Learners (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13759-13760. https://doi.org/10.1609/aaai.v34i10.7151
  10. Haddouche, M.; Guedj, B.; Rivasplata, O.; and Shawe-Taylor, J. (2021) PAC-Bayes unleashed: generalisation bounds with unbounded losses, to appear in Entropy.
  11. Maria Perez-Ortiz, Omar Rivasplata, John Shawe-Taylor and Csaba Szepesvari (2021) Tighter risk certificates for neural networks, to appear Journal of Machine Learning Research.
  12. Mhammedi, Z.; Guedj, B.; and Williamson, R. C., PAC-Bayesian Bound for the Conditional Value at Risk. In NeurIPS, 2020.
  13. T.P.D. Homem, P.E. Santos, A.H. Reali Costa, R.A.C. Bianchi, R. López de Mántaras. Qualitative Case-Based Reasoning and Learning. Artificial Intelligence 283, 2020. Doi: 10.1016/j.artint.2020.103258.
  14. Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, John Shawe-Taylor, PAC-Bayes Analysis Beyond the Usual Bounds. NeurIPS 2020
  1. Sungho Suh, Haebom Lee, Paul Lukowicz, and Yong Oh Lee. “CEGAN: Classification Enhancement Generative Adversarial Networks for unraveling data imbalance problems.” Neural Networks133 (2021): 69-86.
  2. Sungho Suh, Paul Lukowicz, and Yong Oh Lee. “Discriminative feature generation for classification of imbalanced data.” Pattern Recognition(2021): 108302.
  3. Fortes Rey, Vitor, Kamalveer Kaur Garewal, and Paul Lukowicz. “Translating Videos into Synthetic Training Data for Wearable Sensor-Based Activity Recognition Systems Using Residual Deep Convolutional Networks.” Applied Sciences 11.7 (2021): 3094.