9 papers
Deployment-Side Adaptiveness in Multi-Horizon Volatility Forecasting
Riku Green, Zahraa S. Abdallah, Telmo M Silva Filho
In financial forecasting, predictive performance depends not only on which model is trained, but also on how the trained model is deployed. We study this issue in multi-horizon vol…
Exposure Bias as Epistemic Underidentification in Recursive Forecasting
Riku Green, Zahraa S. Abdallah, Telmo M Silva Filho
Recursive multi-step forecasting is usually framed as distribution shift: models are trained on observed histories but deployed on their own predictions. We show this framing is in…
Mind the Gap? A Distributional Comparison of Real and Synthetic Priors for Tabular Foundation Models
Alex O. Davies, Telmo de Menezes e Silva Filho, Nirav Ajmeri
Tabular foundation models are pre-trained on one of three classes of corpus: curated datasets drawn from benchmark repositories, tables harvested at scale from the web, or syntheti…
Continual learning and refinement of causal models through dynamic predicate invention
Enrique Crespo-Fernandez, Oliver Ray, Telmo de Menezes e Silva Filho +1
Efficiently navigating complex environments requires agents to internalize the underlying logic of their world, yet standard world modelling methods often struggle with sample inef…
LEMON: Local Explanations via Modality-aware OptimizatioN
Yu Qin, Phillip Sloan, Raul Santos-Rodriguez +2
Multimodal models are ubiquitous, yet existing explainability methods are often single-modal, architecture-dependent, or too computationally expensive to run at scale. We introduce…
Epistemic Error Decomposition for Multi-step Time Series Forecasting: Rethinking Bias-Variance in Recursive and Direct Strategies
Riku Green, Huw Day, Zahraa S. Abdallah +1
Multi-step forecasting is often described through a simple rule of thumb: recursive strategies are said to have high bias and low variance, while direct strategies are said to have…