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ORCIDJinfeng Yi
- > Home > Persons > Jinfeng Yi
Publications
- 2023
[c68]Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi, Cho-Jui Hsieh:
Training Meta-Surrogate Model for Transferable Adversarial Attack. AAAI 2023: 9516-9524- 2022
- [j4]Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
On the Adversarial Robustness of Vision Transformers. Trans. Mach. Learn. Res. 2022 (2022) - [c63]Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
Robust Text CAPTCHAs Using Adversarial Examples. IEEE Big Data 2022: 1495-1504 - 2021
- [c51]Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh:
Fast Certified Robust Training with Short Warmup. NeurIPS 2021: 18335-18349 - [i52]Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
Robust Text CAPTCHAs Using Adversarial Examples. CoRR abs/2101.02483 (2021) - [i50]Rulin Shao, Zhouxing Shi, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
On the Adversarial Robustness of Visual Transformers. CoRR abs/2103.15670 (2021) - [i49]Zhouxing Shi, Yihan Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh:
Fast Certified Robust Training via Better Initialization and Shorter Warmup. CoRR abs/2103.17268 (2021) - [i44]Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi, Cho-Jui Hsieh:
Training Meta-Surrogate Model for Transferable Adversarial Attack. CoRR abs/2109.01983 (2021) - [i42]Yunxiao Qin, Yuanhao Xiong, Jinfeng Yi, Cho-Jui Hsieh:
Adversarial Attack across Datasets. CoRR abs/2110.07718 (2021) - [i41]Rulin Shao, Jinfeng Yi, Pin-Yu Chen, Cho-Jui Hsieh:
How and When Adversarial Robustness Transfers in Knowledge Distillation? CoRR abs/2110.12072 (2021) - 2020
- [j3]Lu Wang
, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Yuan Jiang:
Spanning attack: reinforce black-box attacks with unlabeled data. Mach. Learn. 109(12): 2349-2368 (2020) - [c48]Minhao Cheng, Jinfeng Yi, Pin-Yu Chen, Huan Zhang, Cho-Jui Hsieh:
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples. AAAI 2020: 3601-3608 - [c45]Lu Wang, Xuanqing Liu, Jinfeng Yi, Yuan Jiang, Cho-Jui Hsieh:
Provably Robust Metric Learning. NeurIPS 2020 - [i38]Lu Wang, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Yuan Jiang:
Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data. CoRR abs/2005.04871 (2020) - [i37]Lu Wang, Xuanqing Liu, Jinfeng Yi, Yuan Jiang, Cho-Jui Hsieh:
Provably Robust Metric Learning. CoRR abs/2006.07024 (2020) - 2019
- [c44]Chun-Chen Tu, Pai-Shun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Shin-Ming Cheng:
AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks. AAAI 2019: 742-749 - [c37]Minhao Cheng, Thong Le, Pin-Yu Chen, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh:
Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach. ICLR (Poster) 2019 - [i31]Lu Wang, Xuanqing Liu, Jinfeng Yi, Zhi-Hua Zhou, Cho-Jui Hsieh:
Evaluating the Robustness of Nearest Neighbor Classifiers: A Primal-Dual Perspective. CoRR abs/1906.03972 (2019) - 2018
- [c30]Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh:
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples. AAAI 2018: 10-17 - [c29]Hongge Chen, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Cho-Jui Hsieh:
Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning. ACL (1) 2018: 2587-2597 - [c23]Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel:
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach. ICLR (Poster) 2018 - [i24]Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Dong Su, Yupeng Gao, Cho-Jui Hsieh, Luca Daniel:
Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach. CoRR abs/1801.10578 (2018) - [i22]Minhao Cheng, Jinfeng Yi, Huan Zhang, Pin-Yu Chen, Cho-Jui Hsieh:
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples. CoRR abs/1803.01128 (2018) - [i20]Chun-Chen Tu, Pai-Shun Ting, Pin-Yu Chen, Sijia Liu, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh, Shin-Ming Cheng:
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks. CoRR abs/1805.11770 (2018) - [i17]Minhao Cheng, Thong Le, Pin-Yu Chen, Jinfeng Yi, Huan Zhang, Cho-Jui Hsieh:
Query-Efficient Hard-label Black-box Attack: An Optimization-based Approach. CoRR abs/1807.04457 (2018) - 2017
- [c18]Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, Cho-Jui Hsieh:
ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models. AISec@CCS 2017: 15-26 - [c16]Jinfeng Yi, Cho-Jui Hsieh, Kush R. Varshney, Lijun Zhang, Yao Li:
Scalable Demand-Aware Recommendation. NIPS 2017: 2412-2421 - [i9]Jinfeng Yi, Cho-Jui Hsieh, Kush R. Varshney, Lijun Zhang, Yao Li:
Positive-Unlabeled Demand-Aware Recommendation. CoRR abs/1702.06347 (2017) - [i7]Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, Cho-Jui Hsieh:
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models. CoRR abs/1708.03999 (2017) - [i5]Pin-Yu Chen, Yash Sharma, Huan Zhang, Jinfeng Yi, Cho-Jui Hsieh:
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples. CoRR abs/1709.04114 (2017) - [i4]Hongge Chen, Huan Zhang, Pin-Yu Chen, Jinfeng Yi, Cho-Jui Hsieh:
Show-and-Fool: Crafting Adversarial Examples for Neural Image Captioning. CoRR abs/1712.02051 (2017)
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