5 papers · 1 filter
Decision-Weighted Flow Matching for Contextual Stochastic Optimization
Jize Xie, Haomiao Wu, Qiang Chen +2
Conditional generative models are increasingly used as scenario generators for stochastic optimization, but standard training objectives emphasize uniform distributional fit rather…
Detect by Yourself: Self-Designing Agentic Workflows for Few-Shot Graph Anomaly Detection
Tairan Huang, Qiang Chen, Yili Wang +4
Graph anomaly detection aims to identify anomaly nodes in attributed graphs and plays an important role in real-world applications. However, existing graph anomaly detection method…
LEGATO: Good Identity Unlearning Is Continuous
Qiang Chen, Chun-Wun Cheng, Xiu Su +5
Machine unlearning has become a crucial role in enabling generative models trained on large datasets to remove sensitive, private, or copyright-protected data. However, existing ma…
Graph Unlearning Meets Influence-aware Negative Preference Optimization
Qiang Chen, Zhongze Wu, Ang He +6
Recent advancements in graph unlearning models have enhanced model utility by preserving the node representation essentially invariant, while using gradient ascent on the forget se…
Identify, Isolate, and Purge: Mitigating Hallucinations in LVLMs via Self-Evolving Distillation
Wenhao Li, Xiu Su, Jingyi Wu +5
Large Vision-Language Models (LVLMs) have demonstrated remarkable advancements in numerous areas such as multimedia. However, hallucination issues significantly limit their credibi…