4 citations · 11 across the 19 of their papers we have counts for
22 papers
An Efficient and Effective Agentic Group Shilling Attack on Recommender Systems
Quoc Viet Nguyen, Trinh Pham, Viet Huynh +4
Recommender systems have become core infrastructure for modern online platforms, personalizing content at scale and strongly influencing what users see, click on, and purchase. How…
Denoising-Aware Inversion: Revealing Privacy Risks in Noise-Protected Text Embeddings
Yubo Wang, Shujie Cui, James Bailey +5
Dense text embeddings are widely used in data mining, retrieval, and downstream machine learning systems due to their compact and semantically rich representations, but recent embe…
Learning to Evaluate: Cost-Effective Model Evaluation on Unlabeled Data with Meta-Learning
Trinh Pham, Viet Huynh, Hongzhi Yin +2
The rapid advancement of machine learning has led to an unprecedented expansion of model ecosystems, making it increasingly difficult to assess the reliability of newly released mo…
An Efficient and Effective Evaluator for Text2SQL Models on Unseen and Unlabeled Data
Trinh Pham, Thanh Tam Nguyen, Viet Huynh +2
Recent advances in large language models have strengthened Text2SQL systems that translate natural language questions into database queries. A persistent deployment challenge is to…
A Multi-agent Text2SQL Framework using Small Language Models and Execution Feedback
Thanh Dat Hoang, Thanh Trung Huynh, Matthias Weidlich +4
Text2SQL, the task of generating SQL queries from natural language text, is a critical challenge in data engineering. Recently, Large Language Models (LLMs) have demonstrated super…
Scaling Text2SQL via LLM-efficient Schema Filtering with Functional Dependency Graph Rerankers
Thanh Dat Hoang, Thanh Tam Nguyen, Thanh Trung Huynh +2
Most modern Text2SQL systems prompt large language models (LLMs) with entire schemas -- mostly column information -- alongside the user's question. While effective on small databas…
Proxy Model-Guided Reinforcement Learning for Client Selection in Federated Recommendation
Liang Qu, Jianxin Li, Wei Yuan +3
Federated recommender systems have emerged as a promising privacy-preserving paradigm, enabling personalized recommendation services without exposing users' raw data. By keeping da…
Controllable and Stealthy Shilling Attacks via Dispersive Latent Diffusion
Shutong Qiao, Wei Yuan, Junliang Yu +3
Recommender systems (RSs) are now fundamental to various online platforms, but their dependence on user-contributed data leaves them vulnerable to shilling attacks that can manipul…
Efficient Multimodal Streaming Recommendation via Expandable Side Mixture-of-Experts
Yunke Qu, Liang Qu, Tong Chen +2
Streaming recommender systems (SRSs) are widely deployed in real-world applications, where user interests shift and new items arrive over time. As a result, effectively capturing u…
Tackling Data Heterogeneity in Federated Time Series Forecasting
Wei Yuan, Guanhua Ye, Xiangyu Zhao +3
Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting.…
Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era
Thanh Tam Nguyen, Zhao Ren, Trinh Pham +4
The rapid advancement of large language models (LLMs) and multimodal learning has transformed digital content creation and manipulation. Traditional visual editing tools require si…
Scalable and Effective Negative Sample Generation for Hyperedge Prediction
Shilin Qu, Weiqing Wang, Yuan-Fang Li +2
Hyperedge prediction is crucial in hypergraph analysis for understanding complex multi-entity interactions in various web-based applications, including social networks and e-commer…
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services
Wei Yuan, Chaoqun Yang, Guanhua Ye +3
Federated sequential recommendation (FedSeqRec) has gained growing attention due to its ability to protect user privacy. Unfortunately, the performance of FedSeqRec is still unsati…
Heterogeneous Hypergraph Embedding for Recommendation Systems
Darnbi Sakong, Viet Hung Vu, Thanh Trung Huynh +4
Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is…
PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System
Wei Yuan, Chaoqun Yang, Liang Qu +3
Sequential recommender systems have made significant progress. Recently, due to increasing concerns about user data privacy, some researchers have implemented federated learning fo…
Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures
Thanh Toan Nguyen, Quoc Viet Hung Nguyen, Thanh Tam Nguyen +4
Recommender systems have become an integral part of online services to help users locate specific information in a sea of data. However, existing studies show that some recommender…
Automated Similarity Metric Generation for Recommendation
Liang Qu, Yun Lin, Wei Yuan +3
The embedding-based architecture has become the dominant approach in modern recommender systems, mapping users and items into a compact vector space. It then employs predefined sim…
A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures
Thanh Tam Nguyen, Thanh Trung Huynh, Zhao Ren +4
As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and exp…
Robust Federated Contrastive Recommender System against Model Poisoning Attack
Wei Yuan, Chaoqun Yang, Liang Qu +3
Federated Recommender Systems (FedRecs) have garnered increasing attention recently, thanks to their privacy-preserving benefits. However, the decentralized and open characteristic…
OntoMedRec: Logically-Pretrained Model-Agnostic Ontology Encoders for Medication Recommendation
Weicong Tan, Weiqing Wang, Xin Zhou +3
Most existing medication recommendation models learn representations for medical concepts based on electronic health records (EHRs) and make recommendations with learnt representat…
Towards Personalized Privacy: User-Governed Data Contribution for Federated Recommendation
Liang Qu, Wei Yuan, Ruiqi Zheng +3
Federated recommender systems (FedRecs) have gained significant attention for their potential to protect user's privacy by keeping user privacy data locally and only communicating…
ROIC-DM: Robust Text Inference and Classification via Diffusion Model
Shilong Yuan, Wei Yuan, Hongzhi Yin +1
While language models have made many milestones in text inference and classification tasks, they remain susceptible to adversarial attacks that can lead to unforeseen outcomes. Exi…