15 citations · 36 across the 10 of their papers we have counts for
6 papers · 1 filter
Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Survey
Yi Zhang, Zhen Chen, Chih-Hong Cheng +6
Text-to-Image (T2I) Diffusion Models (DMs) have garnered widespread attention for their impressive advancements in image generation. However, their growing popularity has raised et…
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory
Sihao Wu, Xingyu Zhao, Xiaowei Huang
Data efficiency of learning, which plays a key role in the Reinforcement Learning (RL) training process, becomes even more important in continual RL with sequential environments. I…
Towards Fairness-Aware Adversarial Learning
Yanghao Zhang, Tianle Zhang, Ronghui Mu +2
Although adversarial training (AT) has proven effective in enhancing the model's robustness, the recently revealed issue of fairness in robustness has not been well addressed, i.e.…
Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting
Yi Dong, Yingjie Wang, Mariana Gama +3
In the realm of power systems, the increasing involvement of residential users in load forecasting applications has heightened concerns about data privacy. Specifically, the load d…
ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion Models against Stochastic Perturbation
Yi Zhang, Yun Tang, Wenjie Ruan +4
Text-to-Image (T2I) Diffusion Models (DMs) have shown impressive abilities in generating high-quality images based on simple text descriptions. However, as is common with many Deep…
Building Guardrails for Large Language Models
Yi Dong, Ronghui Mu, Gaojie Jin +6
As Large Language Models (LLMs) become more integrated into our daily lives, it is crucial to identify and mitigate their risks, especially when the risks can have profound impacts…