21 citations · 55 across the 8 of their papers we have counts for
14 papers
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…
Improving the Adversarial Robustness of NLP Models by Information Bottleneck
Cenyuan Zhang, Xiang Zhou, Yixin Wan +3
Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manip…
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…