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20202026
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cs.LG2026

Discovering Data Manifold Geometry via Non-Contracting Flows

David Vigouroux, Lucas Drumetz, Ronan Fablet +1

We introduce an unsupervised approach for constructing a global reference system by learning, in the ambient space, vector fields that span the tangent spaces of an unknown data ma…

cs.LG2023

Goal Space Abstraction in Hierarchical Reinforcement Learning via Reachability Analysis

Mehdi Zadem, Sergio Mover, Sao Mai Nguyen

Open-ended learning benefits immensely from the use of symbolic methods for goal representation as they offer ways to structure knowledge for efficient and transferable learning. H…

cs.LG2023

Leveraging Neural Koopman Operators to Learn Continuous Representations of Dynamical Systems from Scarce Data

Anthony Frion, Lucas Drumetz, Mauro Dalla Mura +2

Over the last few years, several works have proposed deep learning architectures to learn dynamical systems from observation data with no or little knowledge of the underlying phys…