73 citations · 88 across the 12 of their papers we have counts for
12 papers
DataClawBench: An Agent Benchmark for Exploratory Real-World Financial Data Analysis
Qiaohong Zhang, Weihao Ye, Jialong Chen +7
Autonomous data analysis agents are increasingly expected to conduct exploratory analysis with limited human guidance about data. However, existing benchmarks typically evaluate su…
MemHunter: Automated and Verifiable Memorization Detection at Dataset-scale in LLMs
Zhenpeng Wu, Jian Lou, Zibin Zheng +1
Large language models (LLMs) have been shown to memorize and reproduce content from their training data, raising significant privacy concerns, especially with web-scale datasets. E…
FedBRB: An Effective Solution to the Small-to-Large Scenario in Device-Heterogeneity Federated Learning
Ziyue Xu, Mingfeng Xu, Tianchi Liao +2
Recently, the success of large models has demonstrated the importance of scaling up model size. This has spurred interest in exploring collaborative training of large-scale models…
Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
Yuecheng Li, Lele Fu, Tong Wang +6
To defend against privacy leakage of user data, differential privacy is widely used in federated learning, but it is not free. The addition of noise randomly disrupts the semantic…
Tokenized Model: A Blockchain-Empowered Decentralized Model Ownership Verification Platform
Yihao Li, Yanyi Lai, Tianchi Liao +2
With the development of practical deep learning models like generative AI, their excellent performance has brought huge economic value. For instance, ChatGPT has attracted more tha…
VeryFL: A Verify Federated Learning Framework Embedded with Blockchain
Yihao Li, Yanyi Lai, Chuan Chen +1
Blockchain-empowered federated learning (FL) has provoked extensive research recently. Various blockchain-based federated learning algorithm, architecture and mechanism have been d…
CueGCL: Cluster-aware Personalized Self-Training for Unsupervised Graph Contrastive Learning
Yuecheng Li, Lele Fu, Sheng Huang +3
Recently, graph contrastive learning (GCL) has emerged as one of the optimal solutions for node-level and supervised tasks. However, for structure-related and unsupervised tasks su…
Contrastive Deep Nonnegative Matrix Factorization for Community Detection
Yuecheng Li, Jialong Chen, Chuan Chen +2
Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection, because of its better interpretability. However, the existing NMF-based methods ha…
When ChatGPT Meets Smart Contract Vulnerability Detection: How Far Are We?
Chong Chen, Jianzhong Su, Jiachi Chen +7
With the development of blockchain technology, smart contracts have become an important component of blockchain applications. Despite their crucial role, the development of smart c…
Migrate Demographic Group For Fair GNNs
YanMing Hu, TianChi Liao, JiaLong Chen +3
Graph Neural networks (GNNs) have been applied in many scenarios due to the superior performance of graph learning. However, fairness is always ignored when designing GNNs. As a co…
Multi-View Clustering from the Perspective of Mutual Information
Fu Lele, Zhang Lei, Wang Tong +3
Exploring the complementary information of multi-view data to improve clustering effects is a crucial issue in multi-view clustering. In this paper, we propose a novel model based…
Subspace-Contrastive Multi-View Clustering
Fu Lele, Zhang Lei, Yang Jinghua +3
Most multi-view clustering methods are limited by shallow models without sound nonlinear information perception capability, or fail to effectively exploit complementary information…