1 citations · 2 across the 5 of their papers we have counts for
5 papers
FairRec: Fairness Testing for Deep Recommender Systems
Huizhong Guo, Jinfeng Li, Jingyi Wang +5
Deep learning-based recommender systems (DRSs) are increasingly and widely deployed in the industry, which brings significant convenience to people's daily life in different ways.…
TESTSGD: Interpretable Testing of Neural Networks Against Subtle Group Discrimination
Mengdi Zhang, Jun Sun, Jingyi Wang +1
Discrimination has been shown in many machine learning applications, which calls for sufficient fairness testing before their deployment in ethic-relevant domains such as face reco…
Repairing Adversarial Texts through Perturbation
Guoliang Dong, Jingyi Wang, Jun Sun +5
It is known that neural networks are subject to attacks through adversarial perturbations, i.e., inputs which are maliciously crafted through perturbations to induce wrong predicti…
Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models
Jialuo Chen, Jingyi Wang, Tinglan Peng +6
Deep learning (DL) models, especially those large-scale and high-performance ones, can be very costly to train, demanding a great amount of data and computational resources. Unauth…
Towards Concolic Testing for Hybrid Systems
Pingfan Kong, Yi Li, Xiaohong Chen +3
Hybrid systems exhibit both continuous and discrete behavior. Analyzing hybrid systems is known to be hard. Inspired by the idea of concolic testing (of programs), we investigate w…