5 citations · 6 across the 6 of their papers we have counts for
6 papers
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…
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…
Input-Specific Robustness Certification for Randomized Smoothing
Ruoxin Chen, Jie Li, Junchi Yan +2
Although randomized smoothing has demonstrated high certified robustness and superior scalability to other certified defenses, the high computational overhead of the robustness cer…
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…