6 papers
MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection
Xueying Ding, Simon Klüttermann, Haomin Wen +2
Quality benchmarks are essential for fairly and accurately tracking scientific progress and enabling practitioners to make informed methodological choices. Outlier detection (OD) o…
Defending Against Malicious Finetuning by Scaling Train-time Adversarial Attacks
Haoming Wen, Shi Chen, Qingyu Shi +4
Current open-weight large language models (LLMs) are prone to malicious finetuning attacks, which could compromise the safety alignment of LLMs with only a few steps of supervised…
From Zero to Hero: Advancing Zero-Shot Foundation Models for Tabular Outlier Detection
Xueying Ding, Haomin Wen, Simon Klüttermann +1
Outlier detection (OD) is widely used in practice; but its effective deployment on new tasks is hindered by lack of labeled outliers, which makes algorithm and hyperparameter selec…
FoMo-0D: A Foundation Model for Zero-shot Tabular Outlier Detection
Yuchen Shen, Haomin Wen, Leman Akoglu
Outlier detection (OD) has a vast literature as it finds numerous real-world applications. Being an unsupervised task, model selection is a key bottleneck for OD without label supe…
CoBAD: Modeling Collective Behaviors for Human Mobility Anomaly Detection
Haomin Wen, Shurui Cao, Leman Akoglu
Detecting anomalies in human mobility is essential for applications such as public safety and urban planning. While traditional anomaly detection methods primarily focus on individ…
Uncertainty-aware Human Mobility Modeling and Anomaly Detection
Haomin Wen, Shurui Cao, Zeeshan Rasheed +2
Given the temporal GPS coordinates from a large set of human agents, how can we model their mobility behavior toward effective anomaly (e.g. bad-actor or malicious behavior) detect…