2 papers
cs.LG2026
CIExplainer++: Generating Causal and Interpretable Explanations for Graph Neural Networks
Francisco Caldas, Sahil Satish Kumar, Ruben Belo +1
Explainable Artificial Intelligence aims to make black-box models more trustworthy by presenting, in a human-understandable manner, the elements that lead to the model's output. Th…
stat.ML2026
A Decomposable Forward Process in Diffusion Models for Time-Series Forecasting
Francisco Caldas, Sahil Kumar, Cláudia Soares
We introduce a model-agnostic forward diffusion process for time-series forecasting that decomposes signals into spectral components, preserving structured temporal patterns such a…