40 citations · 100 across the 13 of their papers we have counts for
13 papers
Wasserstein Adversarial Examples on Univariant Time Series Data
Wenjie Wang, Li Xiong, Jian Lou
Adversarial examples are crafted by adding indistinguishable perturbations to normal examples in order to fool a well-trained deep learning model to misclassify. In the context of…
Federated Semi-Supervised Learning with Annotation Heterogeneity
Xinyi Shang, Gang Huang, Yang Lu +4
Federated Semi-Supervised Learning (FSSL) aims to learn a global model from different clients in an environment with both labeled and unlabeled data. Most of the existing FSSL work…
Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels
Qiuchen Zhang, Jing Ma, Jian Lou +2
Deep learning models trained on large-scale data have achieved encouraging performance in many real-world tasks. Meanwhile, publishing those models trained on sensitive datasets, s…
DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy
Qiuchen Zhang, Hong kyu Lee, Jing Ma +3
Graph Neural Networks (GNNs) have achieved great success in learning with graph-structured data. Privacy concerns have also been raised for the trained models which could expose th…
MULTIPAR: Supervised Irregular Tensor Factorization with Multi-task Learning
Yifei Ren, Jian Lou, Li Xiong +3
Tensor factorization has received increasing interest due to its intrinsic ability to capture latent factors in multi-dimensional data with many applications such as recommender sy…
Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data
Yiu-ming Cheung, Juyong Jiang, Feng Yu +1
Despite enormous research interest and rapid application of federated learning (FL) to various areas, existing studies mostly focus on supervised federated learning under the horiz…
Communication Efficient Generalized Tensor Factorization for Decentralized Healthcare Networks
Jing Ma, Qiuchen Zhang, Jian Lou +3
Tensor factorization has been proved as an efficient unsupervised learning approach for health data analysis, especially for computational phenotyping, where the high-dimensional E…
Temporal Network Embedding via Tensor Factorization
Jing Ma, Qiuchen Zhang, Jian Lou +2
Representation learning on static graph-structured data has shown a significant impact on many real-world applications. However, less attention has been paid to the evolving nature…
Integer-arithmetic-only Certified Robustness for Quantized Neural Networks
Haowen Lin, Jian Lou, Li Xiong +1
Adversarial data examples have drawn significant attention from the machine learning and security communities. A line of work on tackling adversarial examples is certified robustne…
SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling
Haowen Lin, Jian Lou, Li Xiong +1
Federated learning enables multiple clients, such as mobile phones and organizations, to collaboratively learn a shared model for prediction while protecting local data privacy. Ho…
RobustFed: A Truth Inference Approach for Robust Federated Learning
Farnaz Tahmasebian, Jian Lou, Li Xiong
Federated learning is a prominent framework that enables clients (e.g., mobile devices or organizations) to train a collaboratively global model under a central server's orchestrat…
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis
Jing Ma, Qiuchen Zhang, Jian Lou +3
Tensor factorization has been demonstrated as an efficient approach for computational phenotyping, where massive electronic health records (EHRs) are converted to concise and meani…
Sturm: Sparse Tubal-Regularized Multilinear Regression for fMRI
Wenwen Li, Jian Lou, Shuo Zhou +1
While functional magnetic resonance imaging (fMRI) is important for healthcare/neuroscience applications, it is challenging to classify or interpret due to its multi-dimensional st…