Public Thesis Defense of Brieuc PINON - ICTEAM
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Computational limitations in Artificial Intelligence algorithms by Brieuc PINON -
Wednesday, 10 September 2025 at 4:30 p.m. - Room BARB94 - Place Sainte-Barbe - 1348 Louvain-la-Neuve
This thesis investigates fundamental limitations and potential opportunities of learning-based Artificial Intelligence algorithms.
A contribution is the comparison of sample efficiency between expressive Deep Learning architectures close to Turing-complete systems of computation, to simpler architectures stacking linear operators and non-linear activation functions. We conclude that a detailed characterization of potential gains is tightly linked to open problems in Computational Complexity, but that a common assumption from derandomization research entails a clear advantage to expressive architectures.
Additionally, we study empirically the relevance of the Transformer architecture and online planning in meta-Reinforcement Learning. Our numerical results suggest that an architecture like the Transformer is useful to model partially observable problem dynamics arising in meta-Reinforcement Learning. Moreover, extensive planning is crucial to handle the exploration and exploitation of the unknown dynamics.
This work also analyzes the interplay between learning and algorithms for problem-solving. We formalize computational limitations through counterexamples for multiple classical algorithms in Reinforcement Learning, such as algorithms treating the model of the dynamics as a black-box and universal value function estimating state-to-state reachability. These fundamental limitations open the possibility of designing more efficient algorithms leveraging Machine Learning for problem-solving.
Jury members
- Prof. Jean-Charles Delvenne (UCLouvain) (Supervisor)
- Prof. Raphaël Jungers (UCLouvain) (Supervisor)
- Prof. Pierre-Antoine Absil (UCLouvain) (Chairperson)
- Prof. Marco Saerens (UCLouvain) (Secretary)
- Prof. Vincent François-Lavet (VU Amsterdam)
- Prof. Johan Suykens (KU Leuven)