6 citations · 21 across the 12 of their papers we have counts for
4 papers · 1 filter
Eliminating Information Leakage in Hard Concept Bottleneck Models with Supervised, Hierarchical Concept Learning
Ao Sun, Yuanyuan Yuan, Pingchuan Ma +1
Concept Bottleneck Models (CBMs) aim to deliver interpretable and interventionable predictions by bridging features and labels with human-understandable concepts. While recent CBMs…
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees
Banghua Zhu, Lun Wang, Qi Pang +4
We propose Byzantine-robust federated learning protocols with nearly optimal statistical rates. In contrast to prior work, our proposed protocols improve the dimension dependence a…
Provably Valid and Diverse Mutations of Real-World Media Data for DNN Testing
Yuanyuan Yuan, Qi Pang, Shuai Wang
Deep neural networks (DNNs) often accept high-dimensional media data (e.g., photos, text, and audio) and understand their perceptual content (e.g., a cat). To test DNNs, diverse in…
Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion
Yuanyuan Yuan, Qi Pang, Shuai Wang
Various deep neural network (DNN) coverage criteria have been proposed to assess DNN test inputs and steer input mutations. The coverage is characterized via neurons having certain…