Xuekai Wei

NeurIPS Conference 2025 Conference Paper

A Dynamic Learning Strategy for Dempster-Shafer Theory with Applications in Classification and Enhancement

• Linlin Fan
• Xingyu Liu
• Mingliang Zhou
• Xuekai Wei
• Weizhi Xian
• Jielu Yan
• Weijia Jia

Effective modelling of uncertain information is crucial for quantifying uncertainty. Dempster–Shafer evidence (DSE) theory is a widely recognized approach for handling uncertain information. However, current methods often neglect the inherent a priori information within data during modelling, and imbalanced data lead to insufficient attention to key information in the model. To address these limitations, this paper presents a dynamic learning strategy based on nonuniform splitting mechanism and Hilbert space mapping. First, the framework uses a nonuniform splitting mechanism to dynamically adjust the weights of data subsets and combines the diffusion factor to effectively incorporate the data a priori information, thereby flexibly addressing uncertainty and conflict. Second, the conflict in the information fusion process is reduced by Hilbert space mapping. Experimental results on multiple tasks show that the proposed method significantly outperforms state-of-the-art methods and effectively improves the performance of classification and low-light image enhancement (LLIE) tasks. The code is available at https: //anonymous. 4open. science/r/Third-ED16.

EAAI Journal 2025 Journal Article

Continuous reinforcement learning via advantage value difference reward shaping: A proximal policy optimization perspective

• Jiawei Lin
• Xuekai Wei
• Weizhi Xian
• Jielu Yan
• Leong Hou U
• Yong Feng
• Zhaowei Shang
• Mingliang Zhou

AAAI Conference 2025 Conference Paper

Tokenphormer: Structure-aware Multi-token Graph Transformer for Node Classification

• Zijie Zhou
• Zhaoqi Lu
• Xuekai Wei
• Rongqin Chen
• Shenghui Zhang
• Pak Lon Ip
• Leong Hou U

Graph Neural Networks (GNNs) are widely used in graph data mining tasks. Traditional GNNs follow a message passing scheme that can effectively utilize local and structural information. However, the phenomena of over-smoothing and over-squashing limit the receptive field in message passing processes. Graph Transformers were introduced to address these issues, achieving a global receptive field but suffering from the noise of irrelevant nodes and loss of structural information. Therefore, drawing inspiration from fine-grained token-based representation learning in Natural Language Processing (NLP), we propose the Structure-aware Multi-token Graph Transformer (Tokenphormer), which generates multiple tokens to effectively capture local and structural information and explore global information at different levels of granularity. Specifically, we first introduce the walk-token generated by mixed walks consisting of four walk types to explore the graph and capture structure and contextual information flexibly. To ensure local and global information coverage, we also introduce the SGPM-token (obtained through the Self-supervised Graph Pre-train Model, SGPM) and the hop-token, extending the length and density limit of the walk-token, respectively. Finally, these expressive tokens are fed into the Transformer model to learn node representations collaboratively. Experimental results demonstrate that the capability of the proposed Tokenphormer can achieve state-of-the-art performance on node classification tasks.