| 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 (*Equal Contribution) Proceedings of the 10th ACM workshop on artificial …, 2017 | 2102 | 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 (NIPS) (oral presentation …, 2017 | 1298 | 2017 |
| Efficient neural network robustness certification with general activation functions H Zhang*, TW Weng*, PY Chen, CJ Hsieh, L Daniel (*Equal Contribution) Advances in Neural Information Processing Systems …, 2018 | 855 | 2018 |
| Towards fast computation of certified robustness for relu networks L Weng*, H Zhang*, H Chen, Z Song, CJ Hsieh, L Daniel, D Boning, ... (*Equal Contribution) International Conference on Machine Learning (ICML …, 2018 | 815 | 2018 |
| Ead: elastic-net attacks to deep neural networks via adversarial examples PY Chen, Y Sharma, H Zhang, J Yi, CJ Hsieh Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 687 | 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 (NeurIPS) (spotlight …, 2019 | 583 | 2019 |
| 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 (*Equal Contribution) International Conference on Learning Representations …, 2018 | 554 | 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 | 518 | 2018 |
| Query-efficient hard-label black-box attack: An optimization-based approach M Cheng, T Le, PY Chen, J Yi, H Zhang, CJ Hsieh International Conference on Learning Representations (ICLR) 2019, 2018 | 481 | 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 (*Equal Contribution) Proceedings of the European Conference on Computer …, 2018 | 457 | 2018 |
| 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 (01), 742-749, 2019 | 447 | 2019 |
| Beta-CROWN: Efficient bound propagation with per-neuron split constraints for complete and incomplete neural network verification S Wang*, H Zhang*, K Xu*, X Lin, S Jana, CJ Hsieh, JZ Kolter (*Equal Contribution) Advances in Neural Information Processing Systems …, 2021 | 383* | 2021 |
| 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 International Conference on Learning Representations (ICLR) 2020, 2019 | 369 | 2019 |
| 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 Journal of Optical Communications and Networking 5 (10), A100-A106, 2013 | 317 | 2013 |
| Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations H Zhang*, H Chen*, C Xiao, B Li, D Boning, CJ Hsieh (*Equal Contribution) NeurIPS (spotlight) 33, 2020 | 308 | 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 | 294 | 2019 |
| 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 (NeurIPS), 9835-9846, 2019 | 279 | 2019 |
| Seq2sick: Evaluating the robustness of sequence-to-sequence models with adversarial examples M Cheng, J Yi, PY Chen, H Zhang, CJ Hsieh Proceedings of the AAAI conference on artificial intelligence 34 (04), 3601-3608, 2020 | 268 | 2020 |
| Automatic perturbation analysis for scalable certified robustness and beyond K Xu*, Z Shi*, H Zhang*, Y Wang, KW Chang, M Huang, B Kailkhura, ... (*Equal Contribution) Advances in Neural Information Processing Systems …, 2020 | 268 | 2020 |
| Adversarial robustness vs. model compression, or both? S Ye, K Xu, S Liu, H Cheng, JH Lambrechts, H Zhang, A Zhou, K Ma, ... Proceedings of the IEEE/CVF International Conference on Computer Vision, 111-120, 2019 | 197 | 2019 |
Cho-Jui HsiehUniversity of California, Los Angeles在 cs.ucla.edu 的电子邮件经过验证
