Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees

We develop a simple and unified framework for nonlinear variable importance estimation that incorporates uncertainty in the prediction function and is compatible with a wide range of machine learning models (e.g., tree ensembles, kernel methods, neural networks, etc).