23 citations · 63 across the 23 of their papers we have counts for
5 papers · 1 filter
SGN: A Similarity-based Generative Network for Data Generation under Distribution Shift
Jiaqi Zhu, Xincheng Chen, Yuncheng Wu +2
Generative models trained on a source domain often produce samples that are poorly aligned with shifted target domains, limiting their effectiveness for target-domain data augmenta…
Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation
Jiaqi Zhu, Shaofeng Cai, Jie Chen +3
Online anomaly detection (OAD) plays a pivotal role in real-time analytics and decision-making for evolving data streams. However, existing methods often rely on costly retraining…
VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection
Fei Xiao, Shaofeng Cai, Gang Chen +3
Fraud detection presents a challenging task characterized by ever-evolving fraud patterns and scarce labeled data. Existing methods predominantly rely on graph-based or sequence-ba…
pTNAS: Progressive Neural Architecture Search for Tabular Data
Naili Xing, Shaofeng Cai, Lingze Zeng +4
Recent advances have shifted the paradigm of tabular learning toward tabular foundation models, yet their accuracy relies on a heavy inference cost that scales poorly with context…
METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection
Jiaqi Zhu, Shaofeng Cai, Fang Deng +2
Real-time analytics and decision-making require online anomaly detection (OAD) to handle drifts in data streams efficiently and effectively. Unfortunately, existing approaches are…