17 citations · 39 across the 10 of their papers we have counts for
11 papers
Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems
Shanglin Wu, Yuyang Luo, Yueqing Liang +4
Large language model (LLM) multi-agent systems can scale along two distinct dimensions: by increasing the number of agents and by improving through accumulated experience over time…
A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents
Wei-Chieh Huang, Weizhi Zhang, Yueqing Liang +56
Research in artificial intelligence is shifting from model innovations and benchmark scores towards problem definition and rigorous real-world evaluation. As the field enters the "…
Can Multimodal LLMs Perform Time Series Anomaly Detection?
Xiongxiao Xu, Haoran Wang, Yueqing Liang +3
Time series anomaly detection (TSAD) has been a long-standing pillar problem in Web-scale systems and online infrastructures, such as service reliability monitoring, system fault d…
Piecing It All Together: Verifying Multi-Hop Multimodal Claims
Haoran Wang, Aman Rangapur, Xiongxiao Xu +4
Existing claim verification datasets often do not require systems to perform complex reasoning or effectively interpret multimodal evidence. To address this, we introduce a new tas…
SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting
Xiongxiao Xu, Canyu Chen, Yueqing Liang +4
Time series forecasting has made significant advances, including with Transformer-based models. The attention mechanism in Transformer effectively captures temporal dependencies by…
Understanding the concerns and choices of public when using large language models for healthcare
Yunpeng Xiao, Kyrie Zhixuan Zhou, Yueqing Liang +1
Large language models (LLMs) have shown their potential in biomedical fields. However, how the public uses them for healthcare purposes such as medical Q\&A, self-diagnosis, and da…
Beyond Detection: Unveiling Fairness Vulnerabilities in Abusive Language Models
Yueqing Liang, Lu Cheng, Ali Payani +1
This work investigates the potential of undermining both fairness and detection performance in abusive language detection. In a dynamic and complex digital world, it is crucial to…
Investigating Gender Euphoria and Dysphoria on TikTok: Characterization and Comparison
SJ Dillon, Yueqing Liang, H. Russell Bernard +1
With the emergence of short video-sharing platforms, engagement with social media sites devoted to opinion and knowledge dissemination has rapidly increased. Among these platforms,…
When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes
Canyu Chen, Yueqing Liang, Xiongxiao Xu +5
Machine learning models have demonstrated promising performance in many areas. However, the concerns that they can be biased against specific demographic groups hinder their adopti…
Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach
Yueqing Liang, Canyu Chen, Tian Tian +1
Modern machine learning (ML) models are becoming increasingly popular and are widely used in decision-making systems. However, studies have shown critical issues of ML discriminati…
Pre-training Graph Neural Network for Cross Domain Recommendation
Chen Wang, Yueqing Liang, Zhiwei Liu +2
A recommender system predicts users' potential interests in items, where the core is to learn user/item embeddings. Nevertheless, it suffers from the data-sparsity issue, which the…