Theoretical Analysis of Adversarial Learning: A Minimax Approach. - Zhuozhuo Tu; Jingwei Zhang; Dacheng Tao (NeurIPS, 2019)

Understanding Generalization in Recurrent Neural Networks. - Zhuozhuo Tu; Fengxiang He; Dacheng Tao (ICLR, 2020)

Explicitly Learning Topology for Differentiable Neural Architecture Search. - Tao Huang 020; Shan You; Yibo Yang; Zhuozhuo Tu; Fei Wang 032; Chen Qian 006; Changshui Zhang (CoRR, 2020)

Towards Defending Multiple Adversarial Perturbations via Gated Batch Normalization. - Aishan Liu; Shiyu Tang; Xianglong Liu 001; Xinyun Chen; Lei Huang 015; Zhuozhuo Tu; Dawn Song; Dacheng Tao (CoRR, 2020)

Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer. - Shiye Lei; Zhuozhuo Tu; Leszek Rutkowski; Feng Zhou 011; Li Shen 008; Fengxiang He; Dacheng Tao (CoRR, 2021)

Few-shot Backdoor Defense Using Shapley Estimation. - Jiyang Guan; Zhuozhuo Tu; Ran He 001; Dacheng Tao (CVPR, 2022)

Theory of Quantum Generative Learning Models with Maximum Mean Discrepancy. - Yuxuan Du; Zhuozhuo Tu; Bujiao Wu; Xiao Yuan 002; Dacheng Tao (CoRR, 2022)

Detecting abnormal connectivity in schizophrenia via a joint directed acyclic graph estimation model. - Gemeng Zhang; Biao Cai; Aiying Zhang; Zhuozhuo Tu; Li Xiao 002; Julia M. Stephen; Tony W. Wilson; Vince D. Calhoun; Yu-Ping Wang 002 (NeuroImage, 2022)

Transition role of entangled data in quantum machine learning. - Xinbiao Wang; Yuxuan Du; Zhuozhuo Tu; Yong Luo 002; Xiao Yuan 002; Dacheng Tao (CoRR, 2023)