Author

Ziyue Qiao

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Open access Apr 2025

Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning

Compared to contemporary or one-feature-one-agent RL-based approaches, HRLFS improves the downstream ML performance with iterative feature subspace exploration while accelerating total runtime by reducing the number of agents involved.

Wei-Liang Zhang, Xiao-Han Huang, Yi Du et al. · ACM Transactions on Knowledg... · 1 citation

Open access Jul 2026

Comprehend, Divide, and Conquer: Feature Subspace Exploration via Multi-Agent Hierarchical Reinforcement Learning

Feature selection aims to preprocess the target dataset, find an optimal and most streamlined feature subset, and enhance the downstream machine learning task. Among filter, wrapper, and embedded-based approaches, the reinforcement learning (RL)-based subspace exploration strategy provides a novel objective optimizatio...

Weiliang Zhang, Xiaohan Huang, Ziyue Qiao et al. · ACM Transactions on Knowledg... · 0 citations

#machine learning Preprint Aug 2026

GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning

This work presents \textsc{GTA-RAG}, a graph-trajectory-augmented RL framework for multi-turn retrieval-augmented reasoning that consistently outperforms RL-based RAG baselines with both Qwen2.5-3B and Qwen2.5-7B backbones, while substantially improving evidence-chain coverage.

Jun Chen, Yongchao Liu, Pengyu Qiu et al. · 0 citations