4 citations · 11 across the 34 of their papers we have counts for
11 papers · 1 filter
DR-Encoder: Encode Low-rank Gradients with Random Prior for Large Language Models Differentially Privately
Huiwen Wu, Deyi Zhang, Xiaohan Li +3
The emergence of the Large Language Model (LLM) has shown their superiority in a wide range of disciplines, including language understanding and translation, relational logic reaso…
Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning
Huiwen Wu, Xiaohan Li, Xiaogang Xu +3
The development of Large Language Models (LLMs) has significantly advanced various AI applications in commercial and scientific research fields, such as scientific literature summa…
Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey
Chiyu Zhang, Lu Zhou, Xiaogang Xu +2
With the advent of Large Vision-Language Models (LVLMs), new attack vectors, such as cognitive bias, prompt injection, and jailbreaking, have emerged. Understanding these attacks p…
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates
Puning Zhao, Jiafei Wu, Zhe Liu +3
We study convex optimization problems under differential privacy (DP). With heavy-tailed gradients, existing works achieve suboptimal rates. The main obstacle is that existing grad…
Learning with User-Level Local Differential Privacy
Puning Zhao, Li Shen, Rongfei Fan +4
User-level privacy is important in distributed systems. Previous research primarily focuses on the central model, while the local models have received much less attention. Under th…
Distinguish Any Fake Videos: Unleashing the Power of Large-scale Data and Motion Features
Lichuan Ji, Yingqi Lin, Zhenhua Huang +5
The development of AI-Generated Content (AIGC) has empowered the creation of remarkably realistic AI-generated videos, such as those involving Sora. However, the widespread adoptio…
Enhancing Learning with Label Differential Privacy by Vector Approximation
Puning Zhao, Rongfei Fan, Huiwen Wu +3
Label differential privacy (DP) is a framework that protects the privacy of labels in training datasets, while the feature vectors are public. Existing approaches protect the priva…
Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition
Xiaogang Xu, Kun Zhou, Tao Hu +4
Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise. In this paper, we present an innovative video decomposition s…
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models
Huiwen Wu, Xiaogang Xu, Deyi Zhang +3
The success of current Large-Language Models (LLMs) hinges on extensive training data that is collected and stored centrally, called Centralized Learning (CL). However, such a coll…
A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy
Puning Zhao, Lifeng Lai, Li Shen +3
Privacy protection of users' entire contribution of samples is important in distributed systems. The most effective approach is the two-stage scheme, which finds a small interval f…
Learn Suspected Anomalies from Event Prompts for Video Anomaly Detection
Chenchen Tao, Xiaohao Peng, Chong Wang +4
Most models for weakly supervised video anomaly detection (WS-VAD) rely on multiple instance learning, aiming to distinguish normal and abnormal snippets without specifying the typ…