Yulin Liu

Zhaojun Ruan, Yulin Liu, Le Fu et al.

Both the level of conservativeness and the computational burden in robust optimization are critically influenced by uncertainty set design. However, contextual side information is rarely exploited in robust dispatch of power systems characterized by irregular data distributions, which hinders the explicit characterization of the relationship between covariates and uncertain parameters. To address this issue, a data-driven method for constructing contextual-aware uncertainty set is proposed in this letter. Based on a conditional Gaussian mixture model, a set of covariates is leveraged as side information to design uncertainty sets tailored to historical data exhibiting irregular distributions. The resulting set is formulated as a union-of-subsets formulation, and a mixed integer linear reformulation is adopted to describe the worst-case realization across all subsets. Finally, the effectiveness of the proposed method is demonstrated through numerical experiments applied to robust unit commitment.

Riting Xia, Rucong Wang, Yulin Liu et al.

Class-imbalanced graph node classification is a practical yet underexplored research problem. Although recent studies have attempted to address this issue, they typically assume clean and reliable labels when processing class-imbalanced graphs. This assumption often violates the nature of real-world graphs, where labels frequently contain noise. Given this gap, this paper systematically investigates robust node classification for class-imbalanced graphs with noisy labels. We propose GraphALP, a novel Graph Augmentation framework based on Large language models (LLMs) and Pseudo-labeling techniques. Specifically, we design an LLM-based oversampling method to generate synthetic minority nodes, producing label-accurate minority nodes to alleviate class imbalance. Based on the class-balanced graphs, we develop a dynamically weighted pseudo-labeling method to obtain high-confidence pseudo labels to reduce label noise ratio. Additionally, we implement a secondary LLM-guided oversampling mechanism to mitigate potential class distribution skew caused by pseudo labels. Experimental results show that GraphALP achieves superior performance over state-of-the-art methods on class-imbalanced graphs with noisy labels.