8 papers
RIDE: An Open Dataset and Benchmark for Train Delay Prediction
Clément Elliker, Mathis Le Bail, Clément Mantoux +2
Train delay prediction is an important problem for both passengers and railway operators, yet progress in the field remains difficult to assess due to the lack of standardized data…
Subspace-Decomposed JEPAs: Disentangling Progression and Content in Latent World Models
Lucas Thil, Jesse Read, Rim Kaddah +1
Joint-Embedding Predictive Architectures (JEPAs) learn compact latent world models by predicting future embeddings, but no single coordinate of the latent is designated to encode t…
STEP: Learning STructured Embeddings for Progressive Time Series
Lucas Thil, Jesse Read, Rim Kaddah +1
We present a novel method for learning interpretable representations of progressive time series, that is, data capturing irreversible state transitions such as degradation or task…
Decoupling Communication from Policy: Robust MARL under Bandwidth Constraints
Alexi Canesse, Benoît Goupil, Jesse Read +1
Communication enables coordination in multi-agent reinforcement learning (MARL), but many real-world applications, e.g., search-and-rescue with drone swarms, operate under severe b…
Parameter-Efficient Distributional RL via Normalizing Flows and a Geometry-Aware Cramér Surrogate
Simo Alami C., Rim Kaddah, Jesse Read +1
Distributional Reinforcement Learning (DistRL) improves upon expectation-based methods by modeling full return distributions, but standard approaches often remain far from parsimon…
A Machine Learning Framework for Turbofan Health Estimation via Inverse Problem Formulation
Milad Leyli-Abadi, Lucas Thil, Sebastien Razakarivony +2
Estimating the health state of turbofan engines is a challenging ill-posed inverse problem, hindered by sparse sensing and complex nonlinear thermodynamics. Research in this area r…