Knockoffs are a popular statistical tool for conditional variable selection in high dimension.

For iterative algorithms, implicit differentiation alleviates this issue but requires custom implementation of Jacobian evaluation. In this paper, we study one-step differentiation, also known as Jacobian-free backpropagation, a method as easy as automatic differentiation and as efficient as implicit differentiation for fast algorithms (e.g., superlinear

The learner's goal is to identify the optimal arm

In recent years, concept-based approaches have emerged as some of the most promising explainability methods to help us interpret the decisions of Artificial Neural Networks (ANNs).

The vast amount of data required to model wildfires at a larger scale presents difficulties in the collection and curation of the data.