most citedGeneral Cutting Planes for Bound-Propagation-Based Neural Network Verification

33 citations · 49 across the 5 of their papers we have counts for

collaborators

5 papers

quant-ph2023

Evaluating the quantum optimal biased bound in a unitary evolution process

Shoukang Chang, Wei Ye, Xuan Rao +6

Seeking the available precision limit of unknown parameters is a significant task in quantum parameter estimation. One often resorts to the widely utilized quantum Cramer-Rao bound…

cs.LG20233 cited

DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing

Jiawei Zhang, Zhongzhu Chen, Huan Zhang +2

Diffusion models have been leveraged to perform adversarial purification and thus provide both empirical and certified robustness for a standard model. On the other hand, different…

cs.LG202310 cited

Calibrating Multimodal Learning

Huan Ma. Qingyang Zhang, Changqing Zhang, Bingzhe Wu +3

Multimodal machine learning has achieved remarkable progress in a wide range of scenarios. However, the reliability of multimodal learning remains largely unexplored. In this paper…

cs.LG20233 cited

Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution Trajectories

Li-Cheng Lan, Huan Zhang, Cho-Jui Hsieh

In this paper, we define, evaluate, and improve the ``relay-generalization'' performance of reinforcement learning (RL) agents on the out-of-distribution ``controllable'' states. I…

cs.LG202233 cited

General Cutting Planes for Bound-Propagation-Based Neural Network Verification

Huan Zhang, Shiqi Wang, Kaidi Xu +5

Bound propagation methods, when combined with branch and bound, are among the most effective methods to formally verify properties of deep neural networks such as correctness, robu…