Huan Zhang
Huan Zhang
UCLA (previously UC Davis)
Verified email at - Homepage
Cited by
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Zoo: Zeroth order optimization based black-box attacks to deep neural networks without training substitute models
PY Chen, H Zhang, Y Sharma, J Yi, CJ Hsieh
Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security …, 2017
Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent
X Lian, C Zhang, H Zhang, CJ Hsieh, W Zhang, J Liu
Advances in Neural Information Processing Systems, 5330-5340, 2017
Ead: elastic-net attacks to deep neural networks via adversarial examples
PY Chen, Y Sharma, H Zhang, J Yi, CJ Hsieh
arXiv preprint arXiv:1709.04114, 2017
Towards fast computation of certified robustness for relu networks
TW Weng, H Zhang, H Chen, Z Song, CJ Hsieh, D Boning, IS Dhillon, ...
arXiv preprint arXiv:1804.09699, 2018
Spectral and spatial 2D fragmentation-aware routing and spectrum assignment algorithms in elastic optical networks
Y Yin, H Zhang, M Zhang, M Xia, Z Zhu, S Dahlfort, SJB Yoo
IEEE/OSA Journal of Optical Communications and Networking 5 (10), A100-A106, 2013
Efficient neural network robustness certification with general activation functions
H Zhang, TW Weng, PY Chen, CJ Hsieh, L Daniel
Advances in neural information processing systems, 4939-4948, 2018
Towards robust neural networks via random self-ensemble
X Liu, M Cheng, H Zhang, CJ Hsieh
Proceedings of the European Conference on Computer Vision (ECCV), 369-385, 2018
Evaluating the robustness of neural networks: An extreme value theory approach
TW Weng, H Zhang, PY Chen, J Yi, D Su, Y Gao, CJ Hsieh, L Daniel
arXiv preprint arXiv:1801.10578, 2018
Is Robustness the Cost of Accuracy?--A Comprehensive Study on the Robustness of 18 Deep Image Classification Models
D Su, H Zhang, H Chen, J Yi, PY Chen, Y Gao
Proceedings of the European Conference on Computer Vision (ECCV), 631-648, 2018
Query-efficient hard-label black-box attack: An optimization-based approach
M Cheng, T Le, PY Chen, J Yi, H Zhang, CJ Hsieh
arXiv preprint arXiv:1807.04457, 2018
Provably robust deep learning via adversarially trained smoothed classifiers
H Salman, J Li, I Razenshteyn, P Zhang, H Zhang, S Bubeck, G Yang
Advances in Neural Information Processing Systems, 11292-11303, 2019
Autozoom: Autoencoder-based zeroth order optimization method for attacking black-box neural networks
CC Tu, P Ting, PY Chen, S Liu, H Zhang, J Yi, CJ Hsieh, SM Cheng
Proceedings of the AAAI Conference on Artificial Intelligence 33, 742-749, 2019
Attacking visual language grounding with adversarial examples: A case study on neural image captioning
H Chen, H Zhang, PY Chen, J Yi, CJ Hsieh
arXiv preprint arXiv:1712.02051, 2017
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples.
M Cheng, J Yi, PY Chen, H Zhang, CJ Hsieh
AAAI, 3601-3608, 2020
Genattack: Practical black-box attacks with gradient-free optimization
M Alzantot, Y Sharma, S Chakraborty, H Zhang, CJ Hsieh, MB Srivastava
Proceedings of the Genetic and Evolutionary Computation Conference, 1111-1119, 2019
Gradient boosted decision trees for high dimensional sparse output
S Si, H Zhang, S Keerthi, D Mahajan, I Dhillon, CJ Hsieh
International conference on machine learning, 2017
Structured adversarial attack: Towards general implementation and better interpretability
K Xu, S Liu, P Zhao, PY Chen, H Zhang, Q Fan, D Erdogmus, Y Wang, ...
arXiv preprint arXiv:1808.01664, 2018
A convex relaxation barrier to tight robustness verification of neural networks
H Salman, G Yang, H Zhang, CJ Hsieh, P Zhang
Advances in Neural Information Processing Systems, 9835-9846, 2019
A comprehensive linear speedup analysis for asynchronous stochastic parallel optimization from zeroth-order to first-order
X Lian, H Zhang, CJ Hsieh, Y Huang, J Liu
Advances in Neural Information Processing Systems, 3054-3062, 2016
Towards stable and efficient training of verifiably robust neural networks
H Zhang, H Chen, C Xiao, S Gowal, R Stanforth, B Li, D Boning, CJ Hsieh
arXiv preprint arXiv:1906.06316, 2019
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