| 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 | 180 | 2018 |
| Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks WL Chiang, X Liu, S Si, Y Li, S Bengio, CJ Hsieh Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 155 | 2019 |
| Adv-bnn: Improved adversarial defense through robust bayesian neural network X Liu, Y Li, C Wu, CJ Hsieh arXiv preprint arXiv:1810.01279, 2018 | 53 | 2018 |
| Rob-gan: Generator, discriminator, and adversarial attacker X Liu, CJ Hsieh Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 33 | 2019 |
| Neural sde: Stabilizing neural ode networks with stochastic noise X Liu, S Si, Q Cao, S Kumar, CJ Hsieh arXiv preprint arXiv:1906.02355, 2019 | 21 | 2019 |
| A unified framework for data poisoning attack to graph-based semi-supervised learning X Liu, S Si, X Zhu, Y Li, CJ Hsieh arXiv preprint arXiv:1910.14147, 2019 | 13 | 2019 |
| Si Si, Qin Cao, Sanjiv Kumar, and Cho-Jui Hsieh. Neural sde: Stabilizing neural ode networks with stochastic noise X Liu, T Xiao arXiv preprint arXiv:1906.02355, 2019 | 12 | 2019 |
| An inexact subsampled proximal Newton-type method for large-scale machine learning X Liu, CJ Hsieh, JD Lee, Y Sun arXiv preprint arXiv:1708.08552, 2017 | 11 | 2017 |
| Evaluating the robustness of nearest neighbor classifiers: A primal-dual perspective L Wang, X Liu, J Yi, ZH Zhou, CJ Hsieh arXiv preprint arXiv:1906.03972, 2019 | 10 | 2019 |
| Stochastic second-order methods for non-convex optimization with inexact Hessian and gradient L Liu, X Liu, CJ Hsieh, D Tao arXiv preprint arXiv:1809.09853, 2018 | 9 | 2018 |
| Learning to encode position for transformer with continuous dynamical model X Liu, HF Yu, I Dhillon, CJ Hsieh International Conference on Machine Learning, 6327-6335, 2020 | 7 | 2020 |
| Graphdefense: Towards robust graph convolutional networks X Wang, X Liu, CJ Hsieh arXiv preprint arXiv:1911.04429, 2019 | 7 | 2019 |
| Gradient boosting neural networks: Grownet S Badirli, X Liu, Z Xing, A Bhowmik, K Doan, SS Keerthi arXiv preprint arXiv:2002.07971, 2020 | 6 | 2020 |
| How Does Noise Help Robustness? Explanation and Exploration under the Neural SDE Framework X Liu, S Si, Q Cao, S Kumar, CJ Hsieh Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 4 | 2020 |
| From adversarial training to generative adversarial networks X Liu, CJ Hsieh | 4 | 2018 |
| Fast variance reduction method with stochastic batch size X Liu, CJ Hsieh International Conference on Machine Learning, 3179-3188, 2018 | 4 | 2018 |
| Evaluations and Methods for Explanation through Robustness Analysis CY Hsieh, CK Yeh, X Liu, P Ravikumar, S Kim, S Kumar, CJ Hsieh arXiv preprint arXiv:2006.00442, 2020 | 3 | 2020 |
| Improving the Speed and Quality of GAN by Adversarial Training J Zhong, X Liu, CJ Hsieh arXiv preprint arXiv:2008.03364, 2020 | 2 | 2020 |
| Provably robust metric learning L Wang, X Liu, J Yi, Y Jiang, CJ Hsieh arXiv preprint arXiv:2006.07024, 2020 | 1 | 2020 |
| How much progress have we made in neural network training? A New Evaluation Protocol for Benchmarking Optimizers Y Xiong, X Liu, LC Lan, Y You, S Si, CJ Hsieh arXiv preprint arXiv:2010.09889, 2020 | | 2020 |
Cho-Jui HsiehUniversity of California, Los AngelesVerified email at cs.ucla.edu
