Zobrazeno 1 - 10
of 250
pro vyhledávání: '"He, Jingrui"'
As data privacy and security attract increasing attention, Federated Recommender System (FRS) offers a solution that strikes a balance between providing high-quality recommendations and preserving user privacy. However, the presence of statistical he
Externí odkaz:
http://arxiv.org/abs/2411.01690
Autor:
Ban, Yikun, Zou, Jiaru, Li, Zihao, Qi, Yunzhe, Fu, Dongqi, Kang, Jian, Tong, Hanghang, He, Jingrui
Link prediction is a critical problem in graph learning with broad applications such as recommender systems and knowledge graph completion. Numerous research efforts have been directed at solving this problem, including approaches based on similarity
Externí odkaz:
http://arxiv.org/abs/2411.01410
Missing data imputation is a critical challenge in tabular datasets, especially in healthcare, where data completeness is vital for accurate analysis. Large language models (LLMs), trained on vast corpora, have shown strong potential in data generati
Externí odkaz:
http://arxiv.org/abs/2410.21520
Hypergraphs naturally arise when studying group relations and have been widely used in the field of machine learning. There has not been a unified formulation of hypergraphs, yet the recently proposed edge-dependent vertex weights (EDVW) modeling is
Externí odkaz:
http://arxiv.org/abs/2411.03331
Graphs have been widely used in the past decades of big data and AI to model comprehensive relational data. When analyzing a graph's statistical properties, graph laws serve as essential tools for parameterizing its structure. Identifying meaningful
Externí odkaz:
http://arxiv.org/abs/2410.12126
Root Cause Analysis (RCA) is essential for pinpointing the root causes of failures in microservice systems. Traditional data-driven RCA methods are typically limited to offline applications due to high computational demands, and existing online RCA m
Externí odkaz:
http://arxiv.org/abs/2410.10021
Powerful as they are, graph neural networks (GNNs) are known to be vulnerable to distribution shifts. Recently, test-time adaptation (TTA) has attracted attention due to its ability to adapt a pre-trained model to a target domain without re-accessing
Externí odkaz:
http://arxiv.org/abs/2410.06976
Anomaly detection (AD) has been widely studied for decades in many real-world applications, including fraud detection in finance, and intrusion detection for cybersecurity, etc. Due to the imbalanced nature between protected and unprotected groups an
Externí odkaz:
http://arxiv.org/abs/2409.10951
Anomaly detection on graphs plays an important role in many real-world applications. Usually, these data are composed of multiple types (e.g., user information and transaction records for financial data), thus exhibiting view heterogeneity. Therefore
Externí odkaz:
http://arxiv.org/abs/2409.09770
The contextual bandit has been identified as a powerful framework to formulate the recommendation process as a sequential decision-making process, where each item is regarded as an arm and the objective is to minimize the regret of $T$ rounds. In thi
Externí odkaz:
http://arxiv.org/abs/2408.05586