10 citations · 12 across the 12 of their papers we have counts for
13 papers
Spectral Saliency for Machine Unlearning
Cedar Site Bai, Amber Yijia Zheng, Raymond A. Yeh +1
Machine unlearning (MU) aims to remove the influence of specific training data while preserving model utility. As the name suggests, MU can be viewed as the inverse of learning, us…
Moving Alphabet: A Controlled Study of Training Data for Text-to-Video Generation
Amber Yijia Zheng, Lu Liu, Raymond A. Yeh +1
Text-to-video generation has advanced significantly over the past five years through scaling of model size, data, and compute. Unlike model architecture, training data is often und…
Designing to Forget: Deep Semi-parametric Models for Unlearning
Amber Yijia Zheng, Yu-Shan Tai, Raymond A. Yeh
Recent advances in machine unlearning have focused on developing algorithms to remove specific training samples from a trained model. In contrast, we observe that not all models ar…
WebAccessVL: Violation-Aware VLM for Web Accessibility
Amber Yijia Zheng, Jae Joong Lee, Bedrich Benes +1
We present a vision-language model (VLM) that automatically edits website HTML to address violations of the Web Content Accessibility Guidelines 2 (WCAG2) while preserving the orig…
Knowledge Distillation Detection for Open-weights Models
Qin Shi, Amber Yijia Zheng, Qifan Song +1
We propose the task of knowledge distillation detection, which aims to determine whether a student model has been distilled from a given teacher, under a practical setting where on…
Model Immunization from a Condition Number Perspective
Amber Yijia Zheng, Cedar Site Bai, Brian Bullins +1
Model immunization aims to pre-train models that are difficult to fine-tune on harmful tasks while retaining their utility on other non-harmful tasks. Though prior work has shown e…