121 citations · 324 across the 17 of their papers we have counts for
7 papers · 1 filter
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
Renjie Pi, Weizhong Zhang, Yueqi Xie +4
The Federated Learning (FL) paradigm is known to face challenges under heterogeneous client data. Local training on non-iid distributed data results in deflected local optimum, whi…
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization
Yueqi Xie, Weizhong Zhang, Renjie Pi +4
Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning (FL) systems. While both of them have attracted great resear…
Equivariant Contrastive Learning for Sequential Recommendation
Peilin Zhou, Jingqi Gao, Yueqi Xie +5
Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augm…
Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting
Juyong Jiang, Binqing Wu, Ling Chen +2
Traffic forecasting is an essential problem in urban planning and computing. The complex dynamic spatial-temporal dependencies among traffic objects (e.g., sensors and road segment…
Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
Peiyan Zhang, Jiayan Guo, Chaozhuo Li +6
Session-based recommendation (SBR) aims to predict the user's next action based on short and dynamic sessions. Recently, there has been an increasing interest in utilizing various…
Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation
Jiayan Guo, Peiyan Zhang, Chaozhuo Li +3
Session-based recommendation (SBR) aims to predict the user next action based on the ongoing sessions. Recently, there has been an increasing interest in modeling the user preferen…
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation
Juyong Jiang, Peiyan Zhang, Yingtao Luo +6
Sequential recommendation (SR) aims to model users dynamic preferences from a series of interactions. A pivotal challenge in user modeling for SR lies in the inherent variability o…