| LIBLINEAR: A library for large linear classification RE Fan, KW Chang, CJ Hsieh, XR Wang, CJ Lin the Journal of machine Learning research 9, 1871-1874, 2008 | 9546 | 2008 |
| 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 | 1484 | 2017 |
| A dual coordinate descent method for large-scale linear SVM CJ Hsieh, KW Chang, CJ Lin, SS Keerthi, S Sundararajan Proceedings of the 25th international conference on Machine learning, 408-415, 2008 | 1161 | 2008 |
| Visualbert: A simple and performant baseline for vision and language LH Li, M Yatskar, D Yin, CJ Hsieh, KW Chang arXiv preprint arXiv:1908.03557, 2019 | 1134 | 2019 |
| 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 30, 2017 | 901 | 2017 |
| 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 | 864 | 2019 |
| Large batch optimization for deep learning: Training bert in 76 minutes Y You, J Li, S Reddi, J Hseu, S Kumar, S Bhojanapalli, X Song, J Demmel, ... arXiv preprint arXiv:1904.00962, 2019 | 719* | 2019 |
| Training and testing low-degree polynomial data mappings via linear SVM. YW Chang, CJ Hsieh, KW Chang, M Ringgaard, CJ Lin Journal of Machine Learning Research 11 (4), 2010 | 648 | 2010 |
| Towards fast computation of certified robustness for relu networks L Weng, H Zhang, H Chen, Z Song, CJ Hsieh, L Daniel, D Boning, ... International Conference on Machine Learning, 5276-5285, 2018 | 636 | 2018 |
| 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 31, 2018 | 575 | 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 | 567 | 2018 |
| Imagenet training in minutes Y You, Z Zhang, CJ Hsieh, J Demmel, K Keutzer Proceedings of the 47th International Conference on Parallel Processing, 1-10, 2018 | 420 | 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 | 418 | 2018 |
| Sparse inverse covariance matrix estimation using quadratic approximation CJ Hsieh, I Dhillon, P Ravikumar, M Sustik Advances in neural information processing systems 24, 2011 | 412 | 2011 |
| 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 | 403 | 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 | 346 | 2018 |
| Coordinate descent method for large-scale l2-loss linear support vector machines. KW Chang, CJ Hsieh, CJ Lin Journal of Machine Learning Research 9 (7), 2008 | 338 | 2008 |
| 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 | 330 | 2019 |
| Scalable coordinate descent approaches to parallel matrix factorization for recommender systems HF Yu, CJ Hsieh, S Si, I Dhillon 2012 IEEE 12th international conference on data mining, 765-774, 2012 | 323 | 2012 |
| A comparison of optimization methods and software for large-scale l1-regularized linear classification GX Yuan, KW Chang, CJ Hsieh, CJ Lin The Journal of Machine Learning Research 11, 3183-3234, 2010 | 299 | 2010 |
