7 citations · 28 across the 10 of their papers we have counts for
8 papers · 1 filter
Finding MNEMON: Reviving Memories of Node Embeddings
Yun Shen, Yufei Han, Zhikun Zhang +5
Previous security research efforts orbiting around graphs have been exclusively focusing on either (de-)anonymizing the graphs or understanding the security and privacy issues of g…
Attack Transferability Characterization for Adversarially Robust Multi-label Classification
Zhuo Yang, Yufei Han, Xiangliang Zhang
Despite of the pervasive existence of multi-label evasion attack, it is an open yet essential problem to characterize the origin of the adversarial vulnerability of a multi-label l…
Characterizing the Evasion Attackability of Multi-label Classifiers
Zhuo Yang, Yufei Han, Xiangliang Zhang
Evasion attack in multi-label learning systems is an interesting, widely witnessed, yet rarely explored research topic. Characterizing the crucial factors determining the attackabi…
Robust Multi-Output Learning with Highly Incomplete Data via Restricted Boltzmann Machines
Giancarlo Fissore, Aurélien Decelle, Cyril Furtlehner +1
In a standard multi-output classification scenario, both features and labels of training data are partially observed. This challenging issue is widely witnessed due to sensor or da…
Prototypical Networks for Multi-Label Learning
Zhuo Yang, Yufei Han, Guoxian Yu +2
We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative…
Robust Federated Training via Collaborative Machine Teaching using Trusted Instances
Yufei Han, Xiangliang Zhang
Federated learning performs distributed model training using local data hosted by agents. It shares only model parameter updates for iterative aggregation at the server. Although i…