86 citations · 255 across the 20 of their papers we have counts for
20 papers
Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks
Zhaohan Xi, Tianyu Du, Changjiang Li +5
Pre-trained language models (PLMs) have demonstrated remarkable performance as few-shot learners. However, their security risks under such settings are largely unexplored. In this…
Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM
Bochuan Cao, Yuanpu Cao, Lu Lin +1
Recently, Large Language Models (LLMs) have made significant advancements and are now widely used across various domains. Unfortunately, there has been a rising concern that LLMs c…
Coskewness under dependence uncertainty
Carole Bernard, Jinghui Chen, Ludger Ruschendorf +1
We study the impact of dependence uncertainty on the expectation of the product of random variables, when for all~. Under some…
On the Vulnerability of Backdoor Defenses for Federated Learning
Pei Fang, Jinghui Chen
Federated Learning (FL) is a popular distributed machine learning paradigm that enables jointly training a global model without sharing clients' data. However, its repetitive serve…
Spectral Augmentation for Self-Supervised Learning on Graphs
Lu Lin, Jinghui Chen, Hongning Wang
Graph contrastive learning (GCL), as an emerging self-supervised learning technique on graphs, aims to learn representations via instance discrimination. Its performance heavily re…
How Powerful is Implicit Denoising in Graph Neural Networks
Songtao Liu, Rex Ying, Hanze Dong +3
Graph Neural Networks (GNNs), which aggregate features from neighbors, are widely used for graph-structured data processing due to their powerful representation learning capabiliti…