3 papers
cs.LG2025
Learning Subgroups with Maximum Treatment Effects without Causal Heuristics
Lincen Yang, Zhong Li, Matthijs van Leeuwen +1
Discovering subgroups with the maximum average treatment effect is crucial for targeted decision making in domains such as precision medicine, public policy, and education. While m…
cs.LG2025
Scalable, Explainable and Provably Robust Anomaly Detection with One-Step Flow Matching
Zhong Li, Qi Huang, Yuxuan Zhu +4
We introduce Time-Conditioned Contraction Matching (TCCM), a novel method for semi-supervised anomaly detection in tabular data. TCCM is inspired by flow matching, a recent generat…
cs.LG2025
Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection
Zhong Li, Yuhang Wang, Matthijs van Leeuwen
Self-supervised learning (SSL) is an emerging paradigm that exploits supervisory signals generated from the data itself, and many recent studies have leveraged SSL to conduct graph…