Machine learning is the art of adapting generic solvers to address specific problems.
I am a Senior Researcher in Machine Learning with the eXascale Infolab, University of Fribourg, Switzerland under prof. Philippe Cudré-Mauroux.
Since 2022, I am affiliated with the Human-IST Institute under prof. Denis Lalanne with the position of Maître Assistant, as Coordinator for the new interdisciplinary Specialized Master in Digital Neuroscience.
I also also teach the courses of Machine Learning (BSc in CS) and Data Analytics in Python (DigitalSkills) as Lecturer.
I obtained my Ph.D. in 2018 (after a non-linear career started in 2009) from the University of Fribourg, under the supervision of prof. Philippe Cudré-Mauroux.
Prior to that I worked for different start-ups (and founded one), and as Research Assistant with both the Dalle Molle Institute for Artificial Intelligence (5y) and with the ICLS group at ZHAW (1y).
I completed both my BSc and MSc (Summa cum Laude) in Computer Science at the University of Milano-Bicocca, in Milan, Italy in 2009.
I have been involved in Machine Learning research since 2008 – for example in
Reinforcement Learning Control,
and addressing challenging real-world applications subject to strict data constraints (low quality / quantity).
My overarching research goal is to solve
Reinforcement Learning Continuous Control,
by any means necessary. This implies creating and improving methods uniquely based on their ability to scale to real-world conditions.
[Click here to read why.]
Some specific topics and challenges include:
- Alternative models to classic Neural Networks, with more desirable features, such as:
- Native Architecture Search for model complexity scalability
- Sub-task specialization and split responsibility
- Advanced memory implements, with focus on object permanence and expert knowledge integration
- Human interpretability, both for memory and control model, for high-responsibility applications
- Alternative model training methods, including:
- High parallelization and scalability for Black-Box Optimization (check out DiBB!)
- Adaptation of Deep Learning to problems with limited data availability
- Advanced reward/fitness shaping, focusing on:
- Competing intrinsic motivation signals
- Self-balancing explicit exploration
- Hyperparameters meta-search
- 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).
[SA22]: Data Analytics in Python [Moodle]
[SP22]: Machine Learning [Moodle]
[SP21]: Machine Learning [Moodle]
[SP20]: Machine Learning [Moodle]
I supervise a limited number of exceptional students depending on current availability. I typically ask to take my course on Machine Learning (or equivalent preparation) first, as it simplifies communication and significantly lowers the time and effort to graduation.
- Available topics
- Real-world control using Direct Policy Search for Reinforcement Learning
- Alternative models to Neural Networks, sidestepping Deep Learning’s limitations
- Efficient distributed Evolutionary Computation: scaling the performance of sophisticated searching in continuous parameter space
- Augmenting control models with new memory implementations focusing on Object Permanence
- Prostate cancer detection using Deep Learning: overcoming data scarcity in medical imaging applications – collaboration with the Hôpital Cantonal de Fribourg [H-FR]
- More available on request.
- MSc: Jonas Fontana:
Sophisticated control by approximating policies with large non-NN models
- 2022.08 MSc - Corina Masanti:
Alternative Models for Direct Policy Search in Reinforcement Learning Control Problems
- 2022.08 MSc - Fabien Vorpe:
Neuroevolution Applications for the DiBB Framework
- 2022.06 MSc - Nicolas Roguet:
Controlling Complex Unstable Robotic Systems Using Direct Policy Search
- 2022.02 BSc - Christophe Broillet:
Pre-Processing Segmentation Datasets for the Hydra Framework
- 2021.08 MSc - Luca Rolshoven:
Study and Extension of the DiBB Framework for Distributing Black-Box Optimization
- 2020.12 MSc - Jiyoung Lee:
P-Hydra: Bridging Transfer Learning And Multitask Learning
- 2020.03 MSc - Johan Jobin & Julien Clément:
Prostate Cancer Classification: A Transfer Learning Approach to Integrate Information From Diverse Body Parts
- 2019.11 BSc - David Bucher:
Data Preparation and Analysis in Support to Cheating Detection: The Case for Economic Momentum in CS:GO
- Giuseppe Cuccu, Luca Rolshoven, Fabien Vorpe, Philippe Cudré-Mauroux, and Tobias Glasmachers. “DiBB: Distributing Black-Box Optimization.” In Proceedings of the Genetic and Evolutionary Computation Conference, 341–49. GECCO ’22. New York, NY, USA: Association for Computing Machinery, 2022. 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
- 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
- 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, 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
- 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, 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 Reinforcement Learning.” In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, 1061–68. GECCO ’13. ACM, 2013. 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
- 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