66 citations · 162 across the 35 of their papers we have counts for
10 papers · 1 filter
Outsmarting the Chameleon: Counterfactual Decoupling for Tactical OOD Shifts in Live Streaming Risk Assessment
Yiran Qiao, Jing Chen, Jiaqi Xu +3
Live streaming has emerged as a primary medium for social interaction and digital commerce, yet it is increasingly plagued by sophisticated risks. A fundamental challenge in this d…
Follow the TRACE: Exploiting Post-Click Trajectories for Online Delayed Conversion Rate Prediction
Xinyue Zhang, Yuanhao Ding, Xiang Ao
Delayed feedback poses a core challenge for online CVR prediction, forcing a trade-off between label accuracy and data freshness. Existing methods address this through delay modeli…
DySCo: Dynamic Semantic Compression for Effective Long-term Time Series Forecasting
Xiang Ao, Yinyu Tan, Mengru Chen
Time series forecasting (TSF) is critical across domains such as finance, meteorology, and energy. While extending the lookback window theoretically provides richer historical cont…
XLinear: Frequency-Enhanced MLP with CrossFilter for Robust Long-Range Forecasting
Xiang Ao
Time series forecasters are widely used across various domains. Among them, MLP (multi-layer perceptron)-based forecasters have been proven to be more robust to noise compared to T…
SDMixer: Sparse Dual-Mixer for Time Series Forecasting
Xiang Ao
Multivariate time series forecasting is widely applied in fields such as transportation, energy, and finance. However, the data commonly suffers from issues of multi-scale characte…
Live or Lie: Action-Aware Capsule Multiple Instance Learning for Risk Assessment in Live Streaming Platforms
Yiran Qiao, Jing Chen, Xiang Ao +3
Live streaming has become a cornerstone of today's internet, enabling massive real-time social interactions. However, it faces severe risks arising from sparse, coordinated malicio…
Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection
Zhiwei Ling, Hailiang Zhao, Chao Zhang +8
Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes while preserving data privacy, making it a cornerstone of intelligent serv…
Beyond the Pre-Service Horizon: Infusing In-Service Behavior for Improved Financial Risk Forecasting
Senhao Liu, Zhiyu Guo, Zhiyuan Ji +5
Typical financial risk management involves distinct phases for pre-service risk assessment and in-service default detection, often modeled separately. This paper proposes a novel f…
LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework
Yiran Qiao, Xiang Ao, Yang Liu +3
Recent prevailing works on graph machine learning typically follow a similar methodology that involves designing advanced variants of graph neural networks (GNNs) to maintain the s…
Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN
Kuan Li, Yang Liu, Xiang Ao +4
Benefiting from the message passing mechanism, Graph Neural Networks (GNNs) have been successful on flourish tasks over graph data. However, recent studies have shown that attacker…