62 citations · 71 across the 8 of their papers we have counts for
6 papers · 1 filter
Understanding Silent Data Corruption in LLM Training
Jeffrey Ma, Hengzhi Pei, Leonard Lausen +1
As the scale of training large language models (LLMs) increases, one emergent failure is silent data corruption (SDC), where hardware produces incorrect computations without explic…
TextGuard: Provable Defense against Backdoor Attacks on Text Classification
Hengzhi Pei, Jinyuan Jia, Wenbo Guo +2
Backdoor attacks have become a major security threat for deploying machine learning models in security-critical applications. Existing research endeavors have proposed many defense…
Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One
Yezhen Wang, Tong Che, Bo Li +4
Autoregressive generative models are commonly used, especially for those tasks involving sequential data. They have, however, been plagued by a slew of inherent flaws due to the in…
Towards Generating Real-World Time Series Data
Hengzhi Pei, Kan Ren, Yuqing Yang +3
Time series data generation has drawn increasing attention in recent years. Several generative adversarial network (GAN) based methods have been proposed to tackle the problem usua…
Improving Certified Robustness via Statistical Learning with Logical Reasoning
Zhuolin Yang, Zhikuan Zhao, Boxin Wang +8
Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently. However, current robustness certification m…
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei +3
This paper studies model-inversion attacks, in which the access to a model is abused to infer information about the training data. Since its first introduction, such attacks have r…