Quantifying Classification Uncertainty using Regularized Evidential Neural Networks. - Xujiang Zhao; Yuzhe Ou; Lance M. Kaplan; Feng Chen 001; Jin-Hee Cho (CoRR, 2019)

Multidimensional Uncertainty-Aware Evidential Neural Networks. - Yibo Hu 002; Yuzhe Ou; Xujiang Zhao; Jin-Hee Cho; Feng Chen 001 (AAAI, 2021)

Generating realistic vulnerabilities via neural code editing: an empirical study. - Yu Nong; Yuzhe Ou; Michael Pradel; Feng Chen 001; Haipeng Cai (ESEC/SIGSOFT FSE, 2022)

VULGEN: Realistic Vulnerability Generation Via Pattern Mining and Deep Learning. - Yu Nong; Yuzhe Ou; Michael Pradel; Feng Chen 001; Haipeng Cai (ICSE, 2023)

Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty. - Changbin Li; Kangshuo Li; Yuzhe Ou; Lance M. Kaplan; Audun Jøsang; Jin-Hee Cho; Dong Hyun Jeong; Feng Chen 001 (ICLR, 2024)