#feature-selection2 articles

2 articles

AINeutralarXiv – CS AI · 4h ago0

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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.

AIBullisharXiv – CS AI · 4h ago0

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Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection

Researchers have developed a new framework for privacy-preserving feature selection that uses permutation-invariant representation learning and federated learning techniques. The approach addresses data imbalance and privacy constraints in distributed scenarios while improving computational efficiency and downstream task performance.