Machine learning is the art of adapting generic solvers to address specific problems.
I am a Senior Researcher and Lecturer at the eXascale Infolab, University of Fribourg, Switzerland.
I obtained my Ph.D. in 2018 from the University of Fribourg under the supervision of Philippe Cudré-Mauroux.
Prior to that I worked for different start-ups (and founded one), and as a Research Assistant with the Dalle Molle Institute for Artificial Intelligence in Lugano, Switzerland.
I completed both my bachelor and master (summa cum laude) at the University of Milano-Bicocca, Italy.
I have been involved with Machine Learning research since 2009 – mostly on
Evolutionary Computation and
My work extends the applicability of Machine Learning towards a broad range of real-world problems, with special focus on high-performance
My main expertise is in
combining Neural Networks and Evolutionary Computation, a technique capable of
direct policy search in reinforcement learning control problems.
My research interests include:
- High parallelization and scalability for sophisticated Evolutionary Algorithms
- Subtask specialization as an alternative to end-to-end network training
- Hybridization of Deep Learning and Neuroevolution
- Best Paper Award. International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019).
- Best Paper Award Runner-Up. Foundation of Digital Games (FDG 2013).
- Best Paper Award. International Conference on Evolutionary Computation (ICEC 2009).
- Undergraduate Fellowship (Master). University of Milano-Bicocca (2006–2009)
- Undergraduate Fellowship (Bachelor). University of Milano-Bicocca (2003–2006)
[SP21]: Machine Learning [Moodle]
[SP20]: Machine Learning [Moodle]
Neuroevolution, Neural Networks, Evolutionary Algorithms, Reinforcement Learning.
- Available topics
- Prostate cancer detection using Deep Learning: overcoming the limitations of medical imaging data – collaboration with the Hôpital Cantonal de Fribourg [HFR]
- Efficient distributed Evolutionary Computation: scaling the performance of sophisticated searching in continuous parameter space
- Non-differentiable model alternatives to Neural Networks leveraging Evolutionary Computation training: sidestepping Deep Learning’s limitations
- Real-world control using Direct Policy Search for Reinforcement Learning: from videogames to autonomous vehicles
- More available upon request
- MSc Fabien Vorpe: Distributed Black Box Optimization
- BSc Christophe Broillet: Data Processing for the Hydra Framework
- 2021.08 MSc - Luca Rolshoven: Study and Extension of the DiBB Framework for Distributing Black-Box Optimization [pdf]
- 2020.12 MSc - Jiyoung Lee: P-Hydra: Bridging Transfer Learning And Multitask Learning [pdf]
- 2020.03 MSc - Johan Jobin & Julien Clément: Prostate Cancer Classification: A Transfer Learning Approach to Integrate Information From Diverse Body Parts [pdf]
- 2019.11 BSc - David Bucher: Data Preparation and Analysis in Support to Cheating Detection: The Case for Economic Momentum in CS:GO [pdf]
- Giuseppe Cuccu, Julian Togelius, and Philippe Cudré-Mauroux. “Playing Atari with Few Neurons.” Autonomous Agents and Multi-Agent Systems 35, no. 2 (2021): 1–23. Bibtex PDF
- Ines Arous, Ljiljana Dolamic, Jie Yang, Akansha Bhardwaj, Giuseppe Cuccu, and Philippe Cudré-Mauroux. “MARTA: Leveraging Human Rationales for Explainable Text Classification.” In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2021). A Virtual Conference, 2021. Bibtex Slides PDF Appendix Code Poster
- Declan Oller, Tobias Glasmachers, and Giuseppe Cuccu. “Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing.” In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and Multiagent Systems, 2020. Bibtex PDF
- Giuseppe Cuccu, Johan Jobin, Julien Clément, Akansha Bhardwaj, Carolin Reischauer, Harriet Thöny, and Philippe Cudré-Mauroux. “Hydra: Cancer Detection Leveraging Multiple Heads and Heterogeneous Datasets.” In 2020 IEEE International Conference on Big Data, BigData, 2020. Bibtex PDF
- Giuseppe Cuccu, Julian Togelius, and Philippe Cudré-Mauroux. “Playing Atari with Six Neurons (Extended Abstract).” In Proceedings of the Twenty-Ninth International Joint Conference On
Artificial Intelligence, IJCAI-20, edited by Christian Bessiere, 4711–15. International Joint Conferences on Artificial Intelligence Organization, 2020. Bibtex Slides PDF
- Giuseppe Cuccu, Julian Togelius, and Philippe Cudré-Mauroux. “Playing Atari with Six Neurons.” In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 998–1006. International Foundation for Autonomous Agents and Multiagent Systems, 2019. Bibtex PDF
- Giuseppe Cuccu. “Extending the Applicability of Neuroevolution.” PhD thesis, Université de Fribourg, 2018. Bibtex PDF
- Giuseppe Cuccu, Somayeh Danafar, Philippe Cudré-Mauroux, Martin Gassner, Stefano Bernero, and Krzysztof Kryszczuk. “A Data-Driven Approach to Predict NOx-Emissions of Gas Turbines.” In 2017 IEEE International Conference on Big Data, BigData 2017, Boston,
MA, USA, December 11-14, 2017, 1283–88, 2017. Bibtex PDF
- Jan Koutník, Giuseppe Cuccu, Jürgen Schmidhuber, and Faustino Gomez. “Evolving Large-Scale Neural Networks for Vision-Based TORCS.” In Foundations of Digital Games (FDG), 206–12, 2013. Bibtex PDF
- Jan Koutník, Giuseppe Cuccu, Jürgen Schmidhuber, and Faustino Gomez. “Evolving Large-Scale Neural Networks for Vision-Based Reinforcement Learning.” In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, 1061–68. GECCO ’13. ACM, 2013. Bibtex PDF
- Giuseppe Cuccu, and Faustino Gomez. “Block Diagonal Natural Evolution Strategies.” In Parallel Problem Solving from Nature - PPSN XII, 488–97. Springer, Berlin, Heidelberg, 2012. Bibtex PDF
- Vincent Graziano, Tobias Glasmachers, Tom Schaul, Leo Pape, Giuseppe Cuccu, Jürgen Leitner, and Jürgen Schmidhuber. “Artificial Curiosity for Autonomous Space Exploration.” Acta Futura 4 (2011): 41–51. Bibtex PDF
- Leonardo Vanneschi, and Giuseppe Cuccu. “Reconstructing Dynamic Target Functions by Means of Genetic Programming Using Variable Population Size.” In Computational Intelligence, 343:121–34. Springer, Berlin, Heidelberg, 2011. Bibtex PDF
- Giuseppe Cuccu, Matthew Luciw, Jürgen Schmidhuber, and Faustino Gomez. “Intrinsically Motivated Neuroevolution for Vision-Based Reinforcement Learning.” In Development and Learning (ICDL), 2011 IEEE International Conference On, 2:1–7. IEEE, 2011. Bibtex PDF
- Giuseppe Cuccu, and Faustino Gomez. “When Novelty Is Not Enough.” In Applications of Evolutionary Computation, 234–43. Springer, Berlin, Heidelberg, 2011. Bibtex PDF
- Giuseppe Cuccu, Faustino Gomez, and Tobias Glasmachers. “Novelty-Based Restarts for Evolution Strategies.” In Evolutionary Computation (CEC), 2011 IEEE Congress On, 158–63. IEEE, 2011. Bibtex PDF
- Leonardo Vanneschi, and Giuseppe Cuccu. “Variable Size Population for Dynamic Optimization with Genetic Programming.” In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, 1895–96. GECCO ’09. ACM, 2009. Bibtex PDF
- Leonardo Vanneschi, and Giuseppe Cuccu. “A Study of Genetic Programming Variable Population Size for Dynamic Optimization Problems.” In International Conference on Evolutionary Computation (ICEC), 119–26, 2009. Bibtex PDF