| 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 | 2281 | 2017 |
| 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 | 1148 | 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 | 865 | 2019 |
| 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 | 713 | 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 | 573 | 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 | 504 | 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 | 472 | 2018 |
| Trustworthy AI: From principles to practices B Li, P Qi, B Liu, S Di, J Liu, J Pei, J Yi, B Zhou ACM Computing Surveys 55 (9), 1-46, 2023 | 470 | 2023 |
| 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 | 469 | 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 | 438 | 2021 |
| Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system T Wei, F Feng, J Chen, Z Wu, J Yi, X He Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 313 | 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 | 305 | 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 | 270 | 2020 |
| On the adversarial robustness of vision transformers R Shao, Z Shi, J Yi, PY Chen, CJ Hsieh arXiv preprint arXiv:2103.15670, 2021 | 233 | 2021 |
| Practical machine learning S Gollapudi Packt Publishing Ltd, 2016 | 226 | 2016 |
| 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 | 151 | 2016 |
| 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 | 144 | 2017 |
| Improved Dynamic Regret for Non-degeneracy Functions L Zhang, T Yang, J Yi, R Jin, ZH Zhou arXiv preprint arXiv:1608.03933, 2016 | 137 | 2016 |
| 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, 353-372, 2015 | 122 | 2015 |
| Self-weighted multiple kernel learning for graph-based clustering and semi-supervised classification Z Kang, X Lu, J Yi, Z Xu arXiv preprint arXiv:1806.07697, 2018 | 112 | 2018 |
Cho-Jui HsiehUniversity of California, Los AngelesPatvirtintas el. paštas cs.ucla.edu