domain adaptation 1expert-guided models 1fake news detection 1knowledge distillation 1multimodal learning 1
From the 1 of 3 linked papers with an AI index.
3 papers
cs.CL2026
Where Detectors Fail: Closing the Tail-Domain Gap with Expert-Guided Mutual Distillation
Xuan Feng, Guihong Liu, Tianlong Gu +5
The paper introduces Expert-Guided Mutual Distillation (EGMD), a method that improves multimodal fake news detection across domains by calibrating input coherence, aligning domain…
cs.LG2025
FairGSE: Fairness-Aware Graph Neural Network without High False Positive Rates
Zhenqiang Ye, Jinjie Lu, Tianlong Gu +2
Graph neural networks (GNNs) have emerged as the mainstream paradigm for graph representation learning due to their effective message aggregation. However, this advantage also ampl…
cs.LG2024
Towards Fair Graph Neural Networks via Graph Counterfactual without Sensitive Attributes
Xuemin Wang, Tianlong Gu, Xuguang Bao +1
Graph-structured data is ubiquitous in today's connected world, driving extensive research in graph analysis. Graph Neural Networks (GNNs) have shown great success in this field, l…