7 citations · 11 across the 13 of their papers we have counts for
14 papers
Scale-Aware Pretraining of Time Series Foundation Models via Multi-Patch Token Alignment and Hybrid Masking
Taihua Chen, Xiang Ma, Yixin Zhang +3
Pretraining time series foundation models across heterogeneous datasets necessitates effective handling of varying sampling frequencies. Current methods either employ dataset-speci…
DepTGL: A Parallel Framework for Memory-based TGNN Training with Adaptive Temporal Data Dependency Management
Linfang Chen, Zhen Song, Lei Liu +6
Memory-based Temporal Graph Neural Networks (M-TGNNs) maintain recursively updated node states to capture fine-grained temporal interactions. However, existing distributed framewor…
Prompt-Unknown Promotion Attacks against LLM-based Sequential Recommender Systems
Yuchuan Zhao, Tong Chen, Junliang Yu +3
Large language model-powered sequential recommender systems (LLM-SRSs) have recently demonstrated remarkable performance, enabling recommendations through prompt-driven inference o…
Coarse-to-Fine Learning of Dynamic Causal Structures
Dezhi Yang, Qiaoyu Tan, Carlotta Domeniconi +3
Learning the dynamic causal structure of time series is a challenging problem. Most existing approaches rely on distributional or structural invariance to uncover underlying causal…
Relational Database Distillation: From Structured Tables to Condensed Graph Data
Xinyi Gao, Jingxi Zhang, Lijian Chen +3
Relational databases (RDBs) underpin the majority of global data management systems, where information is structured into multiple interdependent tables. To effectively use the kno…
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation
Yuchuan Zhao, Tong Chen, Junliang Yu +3
Sequential recommender systems (SRSs) excel in capturing users' dynamic interests, thus playing a key role in various industrial applications. The popularity of SRSs has also drive…
Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts
Haiyang Jiang, Tong Chen, Wentao Zhang +4
Urban flow prediction is a classic spatial-temporal forecasting task that estimates the amount of future traffic flow for a given location. Though models represented by Spatial-Tem…
Epidemiology-informed Network for Robust Rumor Detection
Wei Jiang, Tong Chen, Xinyi Gao +3
The rapid spread of rumors on social media has posed significant challenges to maintaining public trust and information integrity. Since an information cascade process is essential…
New Permutation Decomposition Techniques for Efficient Homomorphic Permutation
Xirong Ma, Junling Fang, Chunpeng Ge +5
Homomorphic permutation is fundamental to privacy-preserving computations based on batch-encoding homomorphic encryption. It underpins nearly all homomorphic matrix operations and…
Physics-guided Active Sample Reweighting for Urban Flow Prediction
Wei Jiang, Tong Chen, Guanhua Ye +4
Urban flow prediction is a spatio-temporal modeling task that estimates the throughput of transportation services like buses, taxis, and ride-sharing, where data-driven models have…
A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender Systems
Hung Vinh Tran, Tong Chen, Quoc Viet Hung Nguyen +3
Since the creation of the Web, recommender systems (RSs) have been an indispensable mechanism in information filtering. State-of-the-art RSs primarily depend on categorical feature…
Lightweight Embeddings for Graph Collaborative Filtering
Xurong Liang, Tong Chen, Lizhen Cui +3
Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods. Meanwhile, owing to the use of an embedding table to represent each user/item…
Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation
Liang Qu, Wei Yuan, Ruiqi Zheng +3
Federated recommender systems (FedRecs) have gained significant attention for their potential to protect user's privacy by keeping user privacy data locally and only communicating…
Decentralized Collaborative Learning with Adaptive Reference Data for On-Device POI Recommendation
Ruiqi Zheng, Liang Qu, Tong Chen +3
In Location-based Social Networks, Point-of-Interest (POI) recommendation helps users discover interesting places. There is a trend to move from the cloud-based model to on-device…