4 accepted papers
Rainfall forecasting presents a dual challenge: extreme zero inflation, where dry days dominate and obscure meaningful precipitation patterns, and pronounced nonstationarity, where climate dynamics evolve across time and regimes. We propose the Deep Extreme Transformer (DET), a principled architectu
Global Climate Models (GCMs) are crucial for predicting future climate changes by simulating the Earth systems. However, GCM outputs exhibit systematic biases due to model uncertainties, parameterization simplifications, and inadequate representation of complex climate phenomena. Traditional bias co
In high-stakes domains such as healthcare, finance, and law, the need for explainable AI is critical. Traditional methods for generating attribution maps, including white-box approaches relying on gradients and black-box techniques that perturb inputs, face challenges like gradient vanishing, blurre…
Estimating causal effects is crucial for decision-makers in many applications, but it is particularly challenging with observational network data due to peer interactions. Some algorithms have been proposed to estimate causal effects involving network data, particularly peer effects, but they often…