1 citations · 1 across the 7 of their papers we have counts for
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Explore with Memory, Learn without It: In-Context Exploration and Self-Distillation for LLM Reasoning
Yutong Chen, Zhichao Xu, Rui Liu +1
Exploration bounds learning efficiency in reinforcement learning with verifiable rewards (RLVR). Current methods explore mostly through independent sampling, without fully using th…
Hierarchical and Permutation-Invariant Feature Transformation Learning via Policy-Guided Embedding Search
Rui Liu, Tao Zhe, Yanyong Huang +5
Feature transformation improves predictive performance on tabular data by constructing informative abstractions from raw features. Recent generative approaches encode transformatio…
Permutation-Invariant Representation Learning for Robust and Privacy-Preserving Feature Selection
Rui Liu, Tao Zhe, Yanjie Fu +3
Feature selection eliminates redundancy among features to improve downstream task performance while reducing computational overhead. Existing methods often struggle to capture intr…
Continuous Optimization for Feature Selection with Permutation-Invariant Embedding and Policy-Guided Search
Rui Liu, Rui Xie, Zijun Yao +2
Feature selection removes redundant features to enhanc performance and computational efficiency in downstream tasks. Existing works often struggle to capture complex feature intera…