| 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 | 1061 | 2017 |
| 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 | 454 | 2018 |
| Symmetric cross entropy for robust learning with noisy labels Y Wang, X Ma, Z Chen, Y Luo, J Yi, J Bailey Proceedings of the IEEE/CVF International Conference on Computer Vision, 322-330, 2019 | 310 | 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 arXiv preprint arXiv:1801.10578, 2018 | 274 | 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 | 267 | 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 | 237 | 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 | 228 | 2019 |
| Improving adversarial robustness requires revisiting misclassified examples Y Wang, D Zou, J Yi, J Bailey, X Ma, Q Gu International Conference on Learning Representations, 2019 | 227 | 2019 |
| On the convergence and robustness of adversarial training Y Wang, X Ma, J Bailey, J Yi, B Zhou, Q Gu arXiv preprint arXiv:2112.08304, 2021 | 175 | 2021 |
| Diverse few-shot text classification with multiple metrics M Yu, X Guo, J Yi, S Chang, S Potdar, Y Cheng, G Tesauro, H Wang, ... arXiv preprint arXiv:1805.07513, 2018 | 155 | 2018 |
| 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 | 154 | 2020 |
| Practical machine learning S Gollapudi Packt Publishing Ltd, 2016 | 123 | 2016 |
| Semi-crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning J Yi, R Jin, A Jain, S Jain, T Yang Advances in Neural Information Processing Systems (NIPS), 1781-1789, 2012 | 99 | 2012 |
| 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 | 91 | 2017 |
| Efficient distance metric learning by adaptive sampling and mini-batch stochastic gradient descent (SGD) Q Qian, R Jin, J Yi, L Zhang, S Zhu Machine Learning 99 (3), 353-372, 2015 | 89 | 2015 |
| Tracking slowly moving clairvoyant: Optimal dynamic regret of online learning with true and noisy gradient T Yang, L Zhang, R Jin, J Yi International Conference on Machine Learning, 449-457, 2016 | 87 | 2016 |
| Robust Ensemble Clustering by Matrix Completion J Yi, T Yang, R Jin, AK Jain, M Mahdavi IEEE International Conference on Data Mining (ICDM), 2012 | 87 | 2012 |
| Efficient Algorithms for Robust One-bit Compressive Sensing L Zhang, J Yi, R Jin International Conference on Machine Learning (ICML), 820-828, 2014 | 82 | 2014 |
| Inferring Users’ Preferences from Crowdsourced Pairwise Comparisons: A Matrix Completion Approach J Yi, R Jin, S Jain, A Jain AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2013 | 72 | 2013 |
| Improved Dynamic Regret for Non-degeneracy Functions L Zhang, T Yang, J Yi, R Jin, ZH Zhou arXiv preprint arXiv:1608.03933, 2016 | 69 | 2016 |
Cho-Jui HsiehUniversity of California, Los AngelesVerified email at cs.ucla.edu
