Continuous Optimization for Feature Selection with Permutation-Invariant Embedding and Policy-Guided Search

Researchers propose a new framework for feature selection that uses permutation-invariant embedding and reinforcement learning to address limitations in current methods. The approach combines an encoder-decoder paradigm to preserve feature relationships without order bias and employs policy-based RL to explore embedding spaces without convexity assumptions.