activity
20152023
most citedImproving Adversarial Robustness via Promoting Ensemble Diversity

190 citations · 197 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2023

Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition

Xiao Yang, Chang Liu, Longlong Xu +5

Face recognition is a prevailing authentication solution in numerous biometric applications. Physical adversarial attacks, as an important surrogate, can identify the weaknesses of…

cs.LG2022★ 7 cited

Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk

Chengyang Ying, Xinning Zhou, Hang Su +3

Though deep reinforcement learning (DRL) has obtained substantial success, it may encounter catastrophic failures due to the intrinsic uncertainty of both transition and observatio…

cs.LG2019★ 190 cited

Improving Adversarial Robustness via Promoting Ensemble Diversity

Tianyu Pang, Kun Xu, Chao Du +2

Though deep neural networks have achieved significant progress on various tasks, often enhanced by model ensemble, existing high-performance models can be vulnerable to adversarial…

stat.ML2017

Message Passing Stein Variational Gradient Descent

Jingwei Zhuo, Chang Liu, Jiaxin Shi +3

Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation…

cs.LG2015

Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation

Bei Chen, Ning Chen, Jun Zhu +2

We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features. Under the generic…