63 citations · 120 across the 12 of their papers we have counts for
13 papers
Contrastive Unlearning: A Contrastive Approach to Machine Unlearning
Hong kyu Lee, Qiuchen Zhang, Carl Yang +2
Machine unlearning aims to eliminate the influence of a subset of training samples (i.e., unlearning samples) from a trained model. Effectively and efficiently removing the unlearn…
Does Differential Privacy Prevent Backdoor Attacks in Practice?
Fereshteh Razmi, Jian Lou, Li Xiong
Differential Privacy (DP) was originally developed to protect privacy. However, it has recently been utilized to secure machine learning (ML) models from poisoning attacks, with DP…
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…
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…
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…
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…
Classification Auto-Encoder based Detector against Diverse Data Poisoning Attacks
Fereshteh Razmi, Li Xiong
Poisoning attacks are a category of adversarial machine learning threats in which an adversary attempts to subvert the outcome of the machine learning systems by injecting crafted…
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…
Federated Graph Classification over Non-IID Graphs
Han Xie, Jing Ma, Li Xiong +1
Federated learning has emerged as an important paradigm for training machine learning models in different domains. For graph-level tasks such as graph classification, graphs can al…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…