<ul><li>Feature selection plays a crucial role in improving performance and efficiency in various tasks by removing redundant features.</li><li>Existing methods integrating generative intelligence have limitations in capturing complex feature interactions and adapting to different scenarios.</li><li>A new framework is proposed to address these limitations by preserving feature subset knowledge in a continuous embedding space while ensuring permutation invariance.</li><li>The framework utilizes an encoder-decoder paradigm and a policy-based reinforcement learning approach to effectively explore the embedding space without relying on strong convex assumptions.</li></ul>

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

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