3 citations · 5 across the 6 of their papers we have counts for
6 papers
Multi-Agent Debate: A Unified Agentic Framework for Tabular Anomaly Detection
Pinqiao Wang, Sheng Li
Tabular anomaly detection is often handled by single detectors or static ensembles, even though strong performance on tabular data typically comes from heterogeneous model families…
Collaborative Optimization of Multiclass Imbalanced Learning: Density-Aware and Region-Guided Boosting
Chuantao Li, Zhi Li, Jiahao Xu +2
Numerous studies on Boosting attempt to mitigate classification bias caused by class imbalance. However, existing studies have yet to explore the collaborative optimization of imba…
Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection
Shouju Wang, Yuchen Song, Sheng'en Li +1
Graph anomaly detection (GAD) has become an increasingly important task across various domains. With the rapid development of graph neural networks (GNNs), GAD methods have achieve…
ID: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition
Shen Li, Jianqing Xu, Jiaying Wu +7
Synthetic face recognition (SFR) aims to generate synthetic face datasets that mimic the distribution of real face data, which allows for training face recognition models in a priv…
Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation
Miao Xiong, Shen Li, Wenjie Feng +3
How do we know when the predictions made by a classifier can be trusted? This is a fundamental problem that also has immense practical applicability, especially in safety-critical…
Identifying through Flows for Recovering Latent Representations
Shen Li, Bryan Hooi, Gim Hee Lee
Identifiability, or recovery of the true latent representations from which the observed data originates, is de facto a fundamental goal of representation learning. Yet, most deep g…