12 citations · 26 across the 3 of their papers we have counts for
4 papers
Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective
Bohang Zhang, Du Jiang, Di He +1
Designing neural networks with bounded Lipschitz constant is a promising way to obtain certifiably robust classifiers against adversarial examples. However, the relevant progress f…
Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis
Jikai Jin, Bohang Zhang, Haiyang Wang +1
Distributionally robust optimization (DRO) is a widely-used approach to learn models that are robust against distribution shift. Compared with the standard optimization setting, th…
Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons
Bohang Zhang, Tianle Cai, Zhou Lu +2
It is well-known that standard neural networks, even with a high classification accuracy, are vulnerable to small -norm bounded adversarial perturbations. Although man…
Improved Analysis of Clipping Algorithms for Non-convex Optimization
Bohang Zhang, Jikai Jin, Cong Fang +1
Gradient clipping is commonly used in training deep neural networks partly due to its practicability in relieving the exploding gradient problem. Recently, \citet{zhang2019gradient…