Codebased for SEFS
SEFS: Self-Supervision Enhanced Feature Selection with Correlated Gates
Authors: Changhee Lee*, Fergus Imrie*, Mihaela van der Schaar
Reference: Changhee Lee, Fergus Imrie, Mihaela van der Schaar, "Self-Supervised Enhanced Feature Selection with Correlated Gates," International Conference on Learning Representations (ICLR), 2022.
Paper Link: https://openreview.net/forum?id=oDFvtxzPOx
Contact: chl8856@gmail.com
This directory contains implementations of SEFS for feature selection (and variable importance) using one synthetic (Block-Structured Noisy Two-Moons) dataset.
To run the SEFS see jupyter-notebook tutorial of SEFS in tutorial.ipynb.