3 citations · 4 across the 5 of their papers we have counts for
6 papers
AdaHAT: Adaptive Hard Attention to the Task in Task-Incremental Learning
Pengxiang Wang, Hongbo Bo, Jun Hong +2
Catastrophic forgetting is a major problem in task-incremental learning, where neural networks tend to overwrite previously learned knowledge when trained on new tasks. A number of…
Influencing LLM Multi-Agent Dialogue via Policy-Parameterized Prompts
Hongbo Bo, Jingyu Hu, Weiru Liu
Large Language Models (LLMs) have emerged as a new paradigm for multi-agent systems. However, existing research on the behaviour of LLM-based multi-agents relies on ad hoc prompts…
Failing on Bias Mitigation: A Case Study on the Challenges of Fairness in Government Data
Hongbo Bo, Jingyu Hu, Debbie Watson +1
The potential for bias and unfairness in AI-supporting government services raises ethical and legal concerns. Using crime rate prediction with the Bristol City Council data as a ca…
Mitigating Degree Bias Adaptively with Hard-to-Learn Nodes in Graph Contrastive Learning
Jingyu Hu, Hongbo Bo, Jun Hong +2
Graph Neural Networks (GNNs) often suffer from degree bias in node classification tasks, where prediction performance varies across nodes with different degrees. Several approaches…
Ego-graph Replay based Continual Learning for Misinformation Engagement Prediction
Hongbo Bo, Ryan McConville, Jun Hong +1
Online social network platforms have a problem with misinformation. One popular way of addressing this problem is via the use of machine learning based automated misinformation det…
Social Influence Prediction with Train and Test Time Augmentation for Graph Neural Networks
Hongbo Bo, Ryan McConville, Jun Hong +1
Data augmentation has been widely used in machine learning for natural language processing and computer vision tasks to improve model performance. However, little research has stud…