5 citations · 17 across the 27 of their papers we have counts for
6 papers · 1 filter
GRAFT: Graph-Tokenized LLMs for Tool Planning
Xinyi Gao, Xinyu Ren, Junliang Yu +3
Large language models (LLMs) are increasingly used to complete complex tasks by selecting and coordinating external tools across multiple steps. This requires aligning tool choices…
Efficient Content-based Recommendation Model Training via Noise-aware Coreset Selection
Hung Vinh Tran, Tong Chen, Hechuan Wen +3
Content-based recommendation systems (CRSs) utilize content features to predict user-item interactions, serving as essential tools for helping users navigate information-rich web s…
Learning Reconfigurable Representations for Multimodal Federated Learning with Missing Data
Duong M. Nguyen, Trong Nghia Hoang, Thanh Trung Huynh +2
Multimodal federated learning in real-world settings often encounters incomplete and heterogeneous data across clients. This results in misaligned local feature representations tha…
Controllable and Stealthy Shilling Attacks via Dispersive Latent Diffusion
Shutong Qiao, Wei Yuan, Junliang Yu +3
Recommender systems (RSs) are now fundamental to various online platforms, but their dependence on user-contributed data leaves them vulnerable to shilling attacks that can manipul…
Tackling Data Heterogeneity in Federated Time Series Forecasting
Wei Yuan, Guanhua Ye, Xiangyu Zhao +3
Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting.…
Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition
Xinyi Gao, Guanhua Ye, Tong Chen +3
The increasing prevalence of large-scale graphs poses a significant challenge for graph neural network training, attributed to their substantial computational requirements. In resp…