6 papers
High-Dimensional Online Change Point Detection with Adaptive Thresholding and Interpretability
Sven Jacob, Bardh Prenkaj, Weijia Shao +1
Change point detection (CPD) identifies abrupt and significant changes in sequential data, with applications in human activity recognition, financial markets, cybersecurity, manufa…
TabSCM: A practical Framework for Generating Realistic Tabular Data
Sven Jacob, Bardh Prenkaj, Weijia Shao +1
Most tabular-data generators match marginal statistics yet ignore causal structure, leading downstream models to learn spurious or unfair patterns. We present TabSCM, a mixed-type…
Stylized Synthetic Augmentation further improves Corruption Robustness
Georg Siedel, Rojan Regmi, Abhirami Anand +3
This paper proposes a training data augmentation pipeline that combines synthetic image data with neural style transfer in order to address the vulnerability of deep vision models…
Structured Universal Adversarial Attacks on Object Detection for Video Sequences
Sven Jacob, Weijia Shao, Gjergji Kasneci
Video-based object detection plays a vital role in safety-critical applications. While deep learning-based object detectors have achieved impressive performance, they remain vulner…
Combined Image Data Augmentations diminish the benefits of Adaptive Label Smoothing
Georg Siedel, Ekagra Gupta, Weijia Shao +2
Soft augmentation regularizes the supervised learning process of image classifiers by reducing label confidence of a training sample based on the magnitude of random-crop augmentat…
Investigating the Corruption Robustness of Image Classifiers with Random Lp-norm Corruptions
Georg Siedel, Weijia Shao, Silvia Vock +1
Robustness is a fundamental property of machine learning classifiers required to achieve safety and reliability. In the field of adversarial robustness of image classifiers, robust…