27 citations · 36 across the 11 of their papers we have counts for
11 papers
LocalGCL: Local-aware Contrastive Learning for Graphs
Haojun Jiang, Jiawei Sun, Jie Li +1
Graph representation learning (GRL) makes considerable progress recently, which encodes graphs with topological structures into low-dimensional embeddings. Meanwhile, the time-cons…
SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning
Jian Zhang, Bowen Li Jie Li, Chentao Wu
In response to legislation mandating companies to honor the \textit{right to be forgotten} by erasing user data, it has become imperative to enable data removal in Vertical Federat…
MARS: Exploiting Multi-Level Parallelism for DNN Workloads on Adaptive Multi-Accelerator Systems
Guan Shen, Jieru Zhao, Zeke Wang +5
Along with the fast evolution of deep neural networks, the hardware system is also developing rapidly. As a promising solution achieving high scalability and low manufacturing cost…
Temporal Gradient Inversion Attacks with Robust Optimization
Bowen Li, Hanlin Gu, Ruoxin Chen +5
Federated Learning (FL) has emerged as a promising approach for collaborative model training without sharing private data. However, privacy concerns regarding information exchanged…
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach
Shijing Yuan, Hongze Liu, Hongtao Lv +4
Cross-silo federated learning (FL) is a typical FL that enables organizations(e.g., financial or medical entities) to train global models on isolated data. Reasonable incentive is…
On Collective Robustness of Bagging Against Data Poisoning
Ruoxin Chen, Zenan Li, Jie Li +2
Bootstrap aggregating (bagging) is an effective ensemble protocol, which is believed can enhance robustness by its majority voting mechanism. Recent works further prove the sample-…
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach
Jiawei Sun, Ruoxin Chen, Jie Li +3
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level…
Generative and Discriminative Learning for Distorted Image Restoration
Yi Gu, Yuting Gao, Jie Li +2
Liquify is a common technique for image editing, which can be used for image distortion. Due to the uncertainty in the distortion variation, restoring distorted images caused by li…
Association: Remind Your GAN not to Forget
Yi Gu, Jie Li, Yuting Gao +5
Neural networks are susceptible to catastrophic forgetting. They fail to preserve previously acquired knowledge when adapting to new tasks. Inspired by human associative memory sys…
A Framework of Randomized Selection Based Certified Defenses Against Data Poisoning Attacks
Ruoxin Chen, Jie Li, Chentao Wu +2
Neural network classifiers are vulnerable to data poisoning attacks, as attackers can degrade or even manipulate their predictions thorough poisoning only a few training samples. H…
FDRC: Flow-Driven Rule Caching Optimization in Software Defined Networking
He Li, Song Guo, Chentao Wu +1
With the sharp growth of cloud services and their possible combinations, the scale of data center network traffic has an inevitable explosive increasing in recent years. Software d…