activity
20242026
collaborators

7 papers

cs.LG2026

ReTaCo: Residual-Target Control for On-Policy Distillation

Zixiang Ni, Zhuo Hu, Renjie Cao +8

On-policy distillation (OPD) trains a student on its own generated prefixes with token-level teacher feedback, but transmitting or storing the teacher's full-vocabulary distributio…

cs.CL2026

Defending Against Malicious Finetuning by Scaling Train-time Adversarial Attacks

Haoming Wen, Shi Chen, Qingyu Shi +4

Malicious finetuning attacks pose a major safety threat against open-weight large language models (LLMs). However, existing alignment-stage defenses provide limited protection agai…

cs.LG2026

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…

cs.LG2026

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…

cs.LG2025

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…

cs.AI2024

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…

cs.LG2024

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…