15 citations · 30 across the 14 of their papers we have counts for
7 papers · 1 filter
BADTV: Unveiling Backdoor Threats in Third-Party Task Vectors
Chia-Yi Hsu, Yu-Lin Tsai, Yu Zhe +6
Task arithmetic in large-scale pre-trained models enables agile adaptation to diverse downstream tasks without extensive retraining. By leveraging task vectors (TVs), users can per…
Safe LoRA: the Silver Lining of Reducing Safety Risks when Fine-tuning Large Language Models
Chia-Yi Hsu, Yu-Lin Tsai, Chih-Hsun Lin +3
While large language models (LLMs) such as Llama-2 or GPT-4 have shown impressive zero-shot performance, fine-tuning is still necessary to enhance their performance for customized…
Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Yu-Lin Tsai, Chia-Yi Hsu, Chulin Xie +6
Diffusion models for text-to-image (T2I) synthesis, such as Stable Diffusion (SD), have recently demonstrated exceptional capabilities for generating high-quality content. However,…
CAFE: Catastrophic Data Leakage in Vertical Federated Learning
Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu +2
Recent studies show that private training data can be leaked through the gradients sharing mechanism deployed in distributed machine learning systems, such as federated learning (F…
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations
Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu +1
Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to…
Adversarial Examples can be Effective Data Augmentation for Unsupervised Machine Learning
Chia-Yi Hsu, Pin-Yu Chen, Songtao Lu +2
Adversarial examples causing evasive predictions are widely used to evaluate and improve the robustness of machine learning models. However, current studies focus on supervised lea…
Non-Singular Adversarial Robustness of Neural Networks
Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu +1
Adversarial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations. While being critical, we argue that solving t…