3 papers
stat.ML2026
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…
cs.LG2026
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…
cs.LG2024
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…