7 papers
Latent Inference-Time Guidance of Time Series Foundation Models
Chloé Hashimoto-Cullen, Amaury Durand, Laurent Bozzi +3
Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks. They are available out-of-the-box and rely on in-context learning to make the…
PAC-Bayesian Reconstruction Guarantees for Time Series Variational Autoencoders
Chloé Hashimoto-Cullen, Ghislain Agoua, Benjamin Guedj +1
Forecasting time series accurately is critical for applications with complex data ranging from energy systems to healthcare and finance. Among current state of the art models, gene…
Cross-Domain Offshore Wind Power Forecasting: Transfer Learning Through Meteorological Clusters
Dominic Weisser, Chloé Hashimoto-Cullen, Benjamin Guedj
Ambitious decarbonisation targets are rapidly increasing the commission of new offshore wind farms. For these newly commissioned plants to run, accurate power forecasts are needed…
Non-Vacuous Generalization Bounds: Can Rescaling Invariances Help?
Damien Rouchouse, Antoine Gonon, Rémi Gribonval +1
A central challenge in understanding generalization is to obtain non-vacuous guarantees that go beyond worst-case complexity over data or weight space. Among existing approaches, P…
RestoreAI -- Pattern-based Risk Estimation Of Remaining Explosives
Björn Kischelewski, Benjamin Guedj, David Wahl
Landmine removal is a slow, resource-intensive process affecting over 60 countries. While AI has been proposed to enhance explosive ordnance (EO) detection, existing methods primar…
AI for Explosive Ordnance Detection in Clearance Operations: The State of Research
Björn Kischelewski, Gregory Cathcart, David Wahl +1
The detection and clearance of explosive ordnance (EO) continues to be a predominantly manual and high-risk process that can benefit from advances in technology to improve its effi…
Predicting Electricity Consumption with Random Walks on Gaussian Processes
Chloé Hashimoto-Cullen, Benjamin Guedj
We consider time-series forecasting problems where data is scarce, difficult to gather, or induces a prohibitive computational cost. As a first attempt, we focus on short-term elec…