no code implementations • 18 Apr 2023 • Mohammad Naseri, Yufei Han, Emiliano De Cristofaro
In VFL, the participants share the same set of training instances but only host a different and non-overlapping subset of the whole feature space.
no code implementations • 13 Dec 2022 • Helene Orsini, Hongyan Bao, Yujun Zhou, Xiangrui Xu, Yufei Han, Longyang Yi, Wei Wang, Xin Gao, Xiangliang Zhang
Machine Learning-as-a-Service systems (MLaaS) have been largely developed for cybersecurity-critical applications, such as detecting network intrusions and fake news campaigns.
no code implementations • 13 Dec 2022 • Hongyan Bao, Yufei Han, Yujun Zhou, Xin Gao, Xiangliang Zhang
Our work targets at searching feasible adversarial perturbation to attack a classifier with high-dimensional categorical inputs in a domain-agnostic setting.
no code implementations • 7 Sep 2022 • Mohammad Naseri, Yufei Han, Enrico Mariconti, Yun Shen, Gianluca Stringhini, Emiliano De Cristofaro
Modern defenses against cyberattacks increasingly rely on proactive approaches, e. g., to predict the adversary's next actions based on past events.
no code implementations • 14 Apr 2022 • Yun Shen, Yufei Han, Zhikun Zhang, Min Chen, Ting Yu, Michael Backes, Yang Zhang, Gianluca Stringhini
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 graph neural networks.
1 code implementation • 15 Dec 2021 • Yun Shen, Xinlei He, Yufei Han, Yang Zhang
Graph neural networks (GNNs), a new family of machine learning (ML) models, have been proposed to fully leverage graph data to build powerful applications.
no code implementations • NeurIPS 2021 • Chu Zhou, Minggui Teng, Yufei Han, Chao Xu, Boxin Shi
Haze, a common kind of bad weather caused by atmospheric scattering, decreases the visibility of scenes and degenerates the performance of computer vision algorithms.
no code implementations • ICLR 2022 • Hongyan Bao, Yufei Han, Yujun Zhou, Yun Shen, Xiangliang Zhang
Characterizing and assessing the adversarial vulnerability of classification models with categorical input has been a practically important, while rarely explored research problem.
1 code implementation • 29 Jun 2021 • Zhuo Yang, Yufei Han, Xiangliang Zhang
We unveil how the transferability level of the attack determines the attackability of the classifier via establishing an information-theoretic analysis of the adversarial risk.
no code implementations • 17 Dec 2020 • Zhuo Yang, Yufei Han, Xiangliang Zhang
Evasion attack in multi-label learning systems is an interesting, widely witnessed, yet rarely explored research topic.
no code implementations • 19 Dec 2019 • Giancarlo Fissore, Aurélien Decelle, Cyril Furtlehner, Yufei Han
In order to take full advantage of these dependencies we consider a purely probabilistic setting in which the features imputation and multi-label classification problems are jointly solved.
no code implementations • 17 Nov 2019 • Zhuo Yang, Yufei Han, Guoxian Yu, Qiang Yang, Xiangliang Zhang
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 data embeddings distribute compactly to form a positive component and negative component respectively, while the positive component and negative component are pushed away from each other.
no code implementations • 8 May 2019 • Yufei Han, Xiangliang Zhang
In our work, we propose a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers, to improve robustness of the federated training process against local data corruption.
no code implementations • 7 May 2019 • Yufei Han, Yuzhe ma, Christopher Gates, Kevin Roundy, Yun Shen
To address these challenges, we formulate collaborative teaching as a consensus and privacy-preserving optimization process to minimize teaching risk.
no code implementations • 7 Jul 2016 • Yufei Han, Maurizio Filippone
The cost of computing the spectrum of Laplacian matrices hinders the application of spectral clustering to large data sets.