A Unified Particle-Optimization Framework for Scalable Bayesian Sampling. - Changyou Chen; Ruiyi Zhang 002; Wenlin Wang; Bai Li 001; Liqun Chen 001 (UAI, 2018)

Second-Order Adversarial Attack and Certifiable Robustness. - Bai Li 001; Changyou Chen; Wenlin Wang; Lawrence Carin (CoRR, 2018)

On Connecting Stochastic Gradient MCMC and Differential Privacy. - Bai Li 001; Changyou Chen; Hao Liu 015; Lawrence Carin (AISTATS, 2019)

Improving Sequence-to-Sequence Learning via Optimal Transport. - Liqun Chen 001; Yizhe Zhang 002; Ruiyi Zhang 002; Chenyang Tao; Zhe Gan; Haichao Zhang; Bai Li 001; Dinghan Shen; Changyou Chen; Lawrence Carin (ICLR (Poster), 2019)

Certified Adversarial Robustness with Additive Noise. - Bai Li 001; Changyou Chen; Wenlin Wang; Lawrence Carin (NeurIPS, 2019)

On Norm-Agnostic Robustness of Adversarial Training. - Bai Li 001; Changyou Chen; Wenlin Wang; Lawrence Carin (CoRR, 2019)

Graph-Driven Generative Models for Heterogeneous Multi-Task Learning. - Wenlin Wang; Hongteng Xu; Zhe Gan; Bai Li 001; Guoyin Wang 002; Liqun Chen 001; Qian Yang 003; Wenqi Wang 001; Lawrence Carin (AAAI, 2020)

Enhancing Cross-Task Black-Box Transferability of Adversarial Examples With Dispersion Reduction. - Yantao Lu; Yunhan Jia; Jianyu Wang; Bai Li 001; Weiheng Chai; Lawrence Carin; Senem Velipasalar (CVPR, 2020)

Towards Practical Lottery Ticket Hypothesis for Adversarial Training. - Bai Li 001; Shiqi Wang 002; Yunhan Jia; Yantao Lu; Zhenyu Zhong; Lawrence Carin; Suman Jana (CoRR, 2020)

Towards Understanding Fast Adversarial Training. - Bai Li 001; Shiqi Wang 002; Suman Jana; Lawrence Carin (CoRR, 2020)

Privacy and Robustness of Deep Neural Networks. - Bai Li 001 (2021)