47 citations · 182 across the 14 of their papers we have counts for
11 papers · 1 filter
CARE: Certifiably Robust Learning with Reasoning via Variational Inference
Jiawei Zhang, Linyi Li, Ce Zhang +1
Despite great recent advances achieved by deep neural networks (DNNs), they are often vulnerable to adversarial attacks. Intensive research efforts have been made to improve the ro…
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
Chulin Xie, Pin-Yu Chen, Qinbin Li +3
Federated learning (FL) enables distributed resource-constrained devices to jointly train shared models while keeping the training data local for privacy purposes. Vertical FL (VFL…
Certifying Some Distributional Fairness with Subpopulation Decomposition
Mintong Kang, Linyi Li, Maurice Weber +3
Extensive efforts have been made to understand and improve the fairness of machine learning models based on observational metrics, especially in high-stakes domains such as medical…
Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines
Bojan Karlaš, David Dao, Matteo Interlandi +4
Developing modern machine learning (ML) applications is data-centric, of which one fundamental challenge is to understand the influence of data quality to ML training -- "Which tra…
Certifying Out-of-Domain Generalization for Blackbox Functions
Maurice Weber, Linyi Li, Boxin Wang +3
Certifying the robustness of model performance under bounded data distribution drifts has recently attracted intensive interest under the umbrella of distributional robustness. How…
LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis
Fan Wu, Yunhui Long, Ce Zhang +1
Graph structured data have enabled several successful applications such as recommendation systems and traffic prediction, given the rich node features and edges information. Howeve…
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks
Nezihe Merve Gürel, Xiangyu Qi, Luka Rimanic +2
Despite the great successes achieved by deep neural networks (DNNs), recent studies show that they are vulnerable against adversarial examples, which aim to mislead DNNs by adding…
TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness
Zhuolin Yang, Linyi Li, Xiaojun Xu +6
Adversarial Transferability is an intriguing property - adversarial perturbation crafted against one model is also effective against another model, while these models are from diff…
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
Boxin Wang, Fan Wu, Yunhui Long +3
Recent success of deep neural networks (DNNs) hinges on the availability of large-scale dataset; however, training on such dataset often poses privacy risks for sensitive training…
Improving Certified Robustness via Statistical Learning with Logical Reasoning
Zhuolin Yang, Zhikuan Zhao, Boxin Wang +8
Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently. However, current robustness certification m…
TSS: Transformation-Specific Smoothing for Robustness Certification
Linyi Li, Maurice Weber, Xiaojun Xu +5
As machine learning (ML) systems become pervasive, safeguarding their security is critical. However, recently it has been demonstrated that motivated adversaries are able to mislea…