121 citations · 210 across the 17 of their papers we have counts for
17 papers
Model Poisoning Attacks to Federated Learning via Multi-Round Consistency
Yueqi Xie, Minghong Fang, Neil Zhenqiang Gong
Model poisoning attacks are critical security threats to Federated Learning (FL). Existing model poisoning attacks suffer from two key limitations: 1) they achieve suboptimal effec…
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation
Peilin Zhou, You-Liang Huang, Yueqi Xie +4
Sequential recommender systems (SRS) are designed to predict users' future behaviors based on their historical interaction data. Recent research has increasingly utilized contrasti…
GradSafe: Detecting Jailbreak Prompts for LLMs via Safety-Critical Gradient Analysis
Yueqi Xie, Minghong Fang, Renjie Pi +1
Large Language Models (LLMs) face threats from jailbreak prompts. Existing methods for detecting jailbreak prompts are primarily online moderation APIs or finetuned LLMs. These str…
MLLM-Protector: Ensuring MLLM's Safety without Hurting Performance
Renjie Pi, Tianyang Han, Jianshu Zhang +6
The deployment of multimodal large language models (MLLMs) has brought forth a unique vulnerability: susceptibility to malicious attacks through visual inputs. This paper investiga…
Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models
Jingwei Yi, Yueqi Xie, Bin Zhu +4
The integration of large language models with external content has enabled applications such as Microsoft Copilot but also introduced vulnerabilities to indirect prompt injection a…
Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case Study
Peilin Zhou, Meng Cao, You-Liang Huang +6
Large Multimodal Models (LMMs) have demonstrated impressive performance across various vision and language tasks, yet their potential applications in recommendation tasks with visu…
LLMRec: Benchmarking Large Language Models on Recommendation Task
Junling Liu, Chao Liu, Peilin Zhou +8
Recently, the fast development of Large Language Models (LLMs) such as ChatGPT has significantly advanced NLP tasks by enhancing the capabilities of conversational models. However,…
Attention Calibration for Transformer-based Sequential Recommendation
Peilin Zhou, Qichen Ye, Yueqi Xie +5
Transformer-based sequential recommendation (SR) has been booming in recent years, with the self-attention mechanism as its key component. Self-attention has been widely believed t…
Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems
Yueqi Xie, Jingqi Gao, Peilin Zhou +5
Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglect…
DYNAFED: Tackling Client Data Heterogeneity with Global Dynamics
Renjie Pi, Weizhong Zhang, Yueqi Xie +4
The Federated Learning (FL) paradigm is known to face challenges under heterogeneous client data. Local training on non-iid distributed data results in deflected local optimum, whi…
Robust Federated Learning against both Data Heterogeneity and Poisoning Attack via Aggregation Optimization
Yueqi Xie, Weizhong Zhang, Renjie Pi +4
Non-IID data distribution across clients and poisoning attacks are two main challenges in real-world federated learning (FL) systems. While both of them have attracted great resear…
Equivariant Contrastive Learning for Sequential Recommendation
Peilin Zhou, Jingqi Gao, Yueqi Xie +5
Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general sequential data augm…
Optimizing Image Compression via Joint Learning with Denoising
Ka Leong Cheng, Yueqi Xie, Qifeng Chen
High levels of noise usually exist in today's captured images due to the relatively small sensors equipped in the smartphone cameras, where the noise brings extra challenges to los…
Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network
Peiyan Zhang, Jiayan Guo, Chaozhuo Li +6
Session-based recommendation (SBR) aims to predict the user's next action based on short and dynamic sessions. Recently, there has been an increasing interest in utilizing various…
Decoupled Side Information Fusion for Sequential Recommendation
Yueqi Xie, Peilin Zhou, Sunghun Kim
Side information fusion for sequential recommendation (SR) aims to effectively leverage various side information to enhance the performance of next-item prediction. Most state-of-t…
IICNet: A Generic Framework for Reversible Image Conversion
Ka Leong Cheng, Yueqi Xie, Qifeng Chen
Reversible image conversion (RIC) aims to build a reversible transformation between specific visual content (e.g., short videos) and an embedding image, where the original content…
Enhanced Invertible Encoding for Learned Image Compression
Yueqi Xie, Ka Leong Cheng, Qifeng Chen
Although deep learning based image compression methods have achieved promising progress these days, the performance of these methods still cannot match the latest compression stand…